{"$schema":"https://raw.githubusercontent.com/oasis-tcs/sarif-spec/master/Schemata/sarif-schema-2.1.0.json","version":"2.1.0","runs":[{"tool":{"driver":{"name":"AiAuditor","informationUri":"https://github.com/drhus/ai-auditor","version":"v0.1.0","rules":[{"id":"eu-ai-act-2024-08/risk-management","name":"eu-ai-act-2024-08/risk-management","shortDescription":{"text":"Risk management system established, implemented, documented"},"fullDescription":{"text":"EU AI Act, Art 9 — Risk management system established, implemented, documented"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-9","safety"],"regulation":"EU AI Act","article":"9","principle":"safety"}},{"id":"eu-ai-act-2024-08/data-governance","name":"eu-ai-act-2024-08/data-governance","shortDescription":{"text":"Data and data governance practices documented"},"fullDescription":{"text":"EU AI Act, Art 10 — Data and data governance practices documented"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-10","privacy"],"regulation":"EU AI Act","article":"10","principle":"privacy"}},{"id":"eu-ai-act-2024-08/technical-documentation","name":"eu-ai-act-2024-08/technical-documentation","shortDescription":{"text":"Technical documentation drawn up before placing on market"},"fullDescription":{"text":"EU AI Act, Art 11 — Technical documentation drawn up before placing on market"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-11","auditability"],"regulation":"EU AI Act","article":"11","principle":"auditability"}},{"id":"eu-ai-act-2024-08/p1-automatic-logs","name":"eu-ai-act-2024-08/p1-automatic-logs","shortDescription":{"text":"Automatic recording of events over the lifetime"},"fullDescription":{"text":"EU AI Act, Art 12(1) — Automatic recording of events over the lifetime"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-12(1)","auditability"],"regulation":"EU AI Act","article":"12(1)","principle":"auditability"}},{"id":"eu-ai-act-2024-08/p2-traceability","name":"eu-ai-act-2024-08/p2-traceability","shortDescription":{"text":"Logging ensures traceability appropriate to risk"},"fullDescription":{"text":"EU AI Act, Art 12(2) — Logging ensures traceability appropriate to risk"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-12(2)","auditability"],"regulation":"EU AI Act","article":"12(2)","principle":"auditability"}},{"id":"eu-ai-act-2024-08/deployer-instructions","name":"eu-ai-act-2024-08/deployer-instructions","shortDescription":{"text":"Transparent operation and instructions for use"},"fullDescription":{"text":"EU AI Act, Art 13 — Transparent operation and instructions for use"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-13","transparency"],"regulation":"EU AI Act","article":"13","principle":"transparency"}},{"id":"eu-ai-act-2024-08/p1-oversight-measures","name":"eu-ai-act-2024-08/p1-oversight-measures","shortDescription":{"text":"Effective human oversight designed and built-in"},"fullDescription":{"text":"EU AI Act, Art 14(1) — Effective human oversight designed and built-in"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-14(1)","human-oversight"],"regulation":"EU AI Act","article":"14(1)","principle":"human-oversight"}},{"id":"eu-ai-act-2024-08/p4e-override","name":"eu-ai-act-2024-08/p4e-override","shortDescription":{"text":"Ability to override / reverse the system's output"},"fullDescription":{"text":"EU AI Act, Art 14(4)(e) — Ability to override / reverse the system's output"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-14(4)(e)","human-oversight"],"regulation":"EU AI Act","article":"14(4)(e)","principle":"human-oversight"}},{"id":"eu-ai-act-2024-08/p1-accuracy","name":"eu-ai-act-2024-08/p1-accuracy","shortDescription":{"text":"Appropriate level of accuracy declared and tested"},"fullDescription":{"text":"EU AI Act, Art 15(1) — Appropriate level of accuracy declared and tested"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-15(1)","safety"],"regulation":"EU AI Act","article":"15(1)","principle":"safety"}},{"id":"eu-ai-act-2024-08/p4-robustness","name":"eu-ai-act-2024-08/p4-robustness","shortDescription":{"text":"Resilience to errors, faults, inconsistencies"},"fullDescription":{"text":"EU AI Act, Art 15(4) — Resilience to errors, faults, inconsistencies"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-15(4)","safety"],"regulation":"EU AI Act","article":"15(4)","principle":"safety"}},{"id":"eu-ai-act-2024-08/p5-cybersecurity","name":"eu-ai-act-2024-08/p5-cybersecurity","shortDescription":{"text":"Cybersecurity measures appropriate to circumstances"},"fullDescription":{"text":"EU AI Act, Art 15(5) — Cybersecurity measures appropriate to circumstances"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-15(5)","security-governance"],"regulation":"EU AI Act","article":"15(5)","principle":"security-governance"}},{"id":"eu-ai-act-2024-08/p6-keep-logs","name":"eu-ai-act-2024-08/p6-keep-logs","shortDescription":{"text":"Deployer log-retention capability supported"},"fullDescription":{"text":"EU AI Act, Art 26(6) — Deployer log-retention capability supported"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-26(6)","accountability"],"regulation":"EU AI Act","article":"26(6)","principle":"accountability"}},{"id":"eu-ai-act-2024-08/p1-ai-disclosure","name":"eu-ai-act-2024-08/p1-ai-disclosure","shortDescription":{"text":"Users informed they are interacting with an AI"},"fullDescription":{"text":"EU AI Act, Art 50(1) — Users informed they are interacting with an AI"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","eu-ai-act-2024-08","article-50(1)","transparency"],"regulation":"EU AI Act","article":"50(1)","principle":"transparency"}},{"id":"nist-ai-rmf-1.0/govern-1.5","name":"nist-ai-rmf-1.0/govern-1.5","shortDescription":{"text":"Ongoing monitoring and periodic review of risk management"},"fullDescription":{"text":"NIST AI RMF, Art GOVERN 1.5 — Ongoing monitoring and periodic review of risk management"},"helpUri":"https://www.nist.gov/itl/ai-risk-management-framework","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","nist-ai-rmf-1.0","article-GOVERN 1.5","accountability"],"regulation":"NIST AI RMF","article":"GOVERN 1.5","principle":"accountability"}},{"id":"nist-ai-rmf-1.0/map-1.1","name":"nist-ai-rmf-1.0/map-1.1","shortDescription":{"text":"Context of use established and understood"},"fullDescription":{"text":"NIST AI RMF, Art MAP 1.1 — Context of use established and understood"},"helpUri":"https://www.nist.gov/itl/ai-risk-management-framework","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","nist-ai-rmf-1.0","article-MAP 1.1","auditability"],"regulation":"NIST AI RMF","article":"MAP 1.1","principle":"auditability"}},{"id":"nist-ai-rmf-1.0/measure-2.3","name":"nist-ai-rmf-1.0/measure-2.3","shortDescription":{"text":"AI system performance evaluated and documented"},"fullDescription":{"text":"NIST AI RMF, Art MEASURE 2.3 — AI system performance evaluated and documented"},"helpUri":"https://www.nist.gov/itl/ai-risk-management-framework","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","nist-ai-rmf-1.0","article-MEASURE 2.3","safety"],"regulation":"NIST AI RMF","article":"MEASURE 2.3","principle":"safety"}},{"id":"nist-ai-rmf-1.0/measure-2.7","name":"nist-ai-rmf-1.0/measure-2.7","shortDescription":{"text":"Security and resilience evaluated"},"fullDescription":{"text":"NIST AI RMF, Art MEASURE 2.7 — Security and resilience evaluated"},"helpUri":"https://www.nist.gov/itl/ai-risk-management-framework","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","nist-ai-rmf-1.0","article-MEASURE 2.7","security-governance"],"regulation":"NIST AI RMF","article":"MEASURE 2.7","principle":"security-governance"}},{"id":"nist-ai-rmf-1.0/manage-4.1","name":"nist-ai-rmf-1.0/manage-4.1","shortDescription":{"text":"Post-deployment monitoring, appeal and override, change management"},"fullDescription":{"text":"NIST AI RMF, Art MANAGE 4.1 — Post-deployment monitoring, appeal and override, change management"},"helpUri":"https://www.nist.gov/itl/ai-risk-management-framework","defaultConfiguration":{"level":"error"},"properties":{"tags":["ai-compliance","nist-ai-rmf-1.0","article-MANAGE 4.1","auditability"],"regulation":"NIST AI RMF","article":"MANAGE 4.1","principle":"auditability"}},{"id":"iso-42001-2023/clause-5.3-roles","name":"iso-42001-2023/clause-5.3-roles","shortDescription":{"text":"Roles, responsibilities and authorities"},"fullDescription":{"text":"ISO/IEC 42001, Art 5.3 — Roles, responsibilities and authorities"},"helpUri":"https://www.iso.org/standard/81230.html","defaultConfiguration":{"level":"note"},"properties":{"tags":["ai-compliance","iso-42001-2023","article-5.3","accountability"],"regulation":"ISO/IEC 42001","article":"5.3","principle":"accountability"}},{"id":"iso-42001-2023/clause-7.5-documented-information","name":"iso-42001-2023/clause-7.5-documented-information","shortDescription":{"text":"Documented information for the AI management system"},"fullDescription":{"text":"ISO/IEC 42001, Art 7.5 — Documented information for the AI management system"},"helpUri":"https://www.iso.org/standard/81230.html","defaultConfiguration":{"level":"note"},"properties":{"tags":["ai-compliance","iso-42001-2023","article-7.5","auditability"],"regulation":"ISO/IEC 42001","article":"7.5","principle":"auditability"}},{"id":"iso-42001-2023/clause-8.1-operational-planning","name":"iso-42001-2023/clause-8.1-operational-planning","shortDescription":{"text":"Operational planning and control"},"fullDescription":{"text":"ISO/IEC 42001, Art 8.1 — Operational planning and control"},"helpUri":"https://www.iso.org/standard/81230.html","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","iso-42001-2023","article-8.1","safety"],"regulation":"ISO/IEC 42001","article":"8.1","principle":"safety"}},{"id":"iso-42001-2023/clause-9.1-monitoring","name":"iso-42001-2023/clause-9.1-monitoring","shortDescription":{"text":"Monitoring, measurement, analysis and evaluation"},"fullDescription":{"text":"ISO/IEC 42001, Art 9.1 — Monitoring, measurement, analysis and evaluation"},"helpUri":"https://www.iso.org/standard/81230.html","defaultConfiguration":{"level":"note"},"properties":{"tags":["ai-compliance","iso-42001-2023","article-9.1","auditability"],"regulation":"ISO/IEC 42001","article":"9.1","principle":"auditability"}},{"id":"iso-42001-2023/annex-a5-internal-org","name":"iso-42001-2023/annex-a5-internal-org","shortDescription":{"text":"Internal organization controls"},"fullDescription":{"text":"ISO/IEC 42001, Art A.5 — Internal organization controls"},"helpUri":"https://www.iso.org/standard/81230.html","defaultConfiguration":{"level":"note"},"properties":{"tags":["ai-compliance","iso-42001-2023","article-A.5","accountability"],"regulation":"ISO/IEC 42001","article":"A.5","principle":"accountability"}},{"id":"iso-42001-2023/annex-a7-resources","name":"iso-42001-2023/annex-a7-resources","shortDescription":{"text":"Resources for AI systems"},"fullDescription":{"text":"ISO/IEC 42001, Art A.7 — Resources for AI systems"},"helpUri":"https://www.iso.org/standard/81230.html","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","iso-42001-2023","article-A.7","security-governance"],"regulation":"ISO/IEC 42001","article":"A.7","principle":"security-governance"}},{"id":"gdpr-2016/art-22-automated-decisions","name":"gdpr-2016/art-22-automated-decisions","shortDescription":{"text":"Automated individual decision-making, including profiling"},"fullDescription":{"text":"GDPR, Art 22 — Automated individual decision-making, including profiling"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2016/679/oj","defaultConfiguration":{"level":"note"},"properties":{"tags":["ai-compliance","gdpr-2016","article-22","human-oversight"],"regulation":"GDPR","article":"22","principle":"human-oversight"}},{"id":"gdpr-2016/art-25-by-design","name":"gdpr-2016/art-25-by-design","shortDescription":{"text":"Data protection by design and by default"},"fullDescription":{"text":"GDPR, Art 25 — Data protection by design and by default"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2016/679/oj","defaultConfiguration":{"level":"note"},"properties":{"tags":["ai-compliance","gdpr-2016","article-25","privacy"],"regulation":"GDPR","article":"25","principle":"privacy"}},{"id":"gdpr-2016/art-32-security","name":"gdpr-2016/art-32-security","shortDescription":{"text":"Security of processing"},"fullDescription":{"text":"GDPR, Art 32 — Security of processing"},"helpUri":"https://eur-lex.europa.eu/eli/reg/2016/679/oj","defaultConfiguration":{"level":"warning"},"properties":{"tags":["ai-compliance","gdpr-2016","article-32","security-governance"],"regulation":"GDPR","article":"32","principle":"security-governance"}}]}},"automationDetails":{"id":"aiauditor/aud_01KS70FB8DCPW4MD6ZHCSK"},"originalUriBaseIds":{"%SRCROOT%":{"uri":"https://github.com/gpt-engineer-org/gpt-engineer/blob/a90fcd543eedcc0ff2c34561bc0785d2ba83c47e/"}},"versionControlProvenance":[{"repositoryUri":"https://github.com/gpt-engineer-org/gpt-engineer","revisionId":"a90fcd543eedcc0ff2c34561bc0785d2ba83c47e","branch":"a90fcd543eedcc0ff2c34561bc0785d2ba83c47e"}],"results":[{"ruleId":"eu-ai-act-2024-08/risk-management","level":"warning","message":{"text":"Risk management system established, implemented, documented — verdict: PARTIAL (35% conf).\n\nComposite raw score 0.30 (1/3 rules matched). Supporting docs may exist outside the repo.\n\nLLM judge (confidence 0.35): CI/CD evaluation gates provide some risk control mechanism, but absence of documented risk register and threat model leaves critical identification, analysis, and systematic review requirements unmet. Human review of external documentation (design files, risk assessments, governance records) is needed to determine full compliance with the continuous iterative risk management lifecycle."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/workflows/ci.yaml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.35,"rawScore":0.3,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-9/risk-management"}},{"ruleId":"eu-ai-act-2024-08/data-governance","level":"error","message":{"text":"Data and data governance practices documented — verdict: FAIL (75% conf).\n\nComposite raw score 0.10 (0/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: \"\"\" response = requests.post(url, json=extra_arguments) return response if __name__ == \"__main__\": URL_BASE = \"http://127.0.0"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"scripts/test_api.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":25,"endLine":25}}}],"properties":{"verdict":"fail","score":0,"confidence":0.75,"rawScore":0.1,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-10/data-governance"}},{"ruleId":"eu-ai-act-2024-08/technical-documentation","level":"error","message":{"text":"Technical documentation drawn up before placing on market — verdict: FAIL (73% conf).\n\nComposite raw score 0.07 (0/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: 1 required sections present"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":0,"confidence":0.725,"rawScore":0.075,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-11/technical-documentation"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: odel_name) logger.debug(f\"Using model {self.model_name}\") def start(self, system: str, user: Any, *, step_name: str) -> List[M"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":118,"endLine":118}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: t=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\".join([m.pretty_repr() for m in messages"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":235,"endLine":235}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: d(response) logger.debug(f\"Chat completion finished: {messages}\") return messages @backoff.on_exception(backoff.expo,"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":249,"endLine":249}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: t=prompt)) logger.debug(f\"Creating a new chat completion: {messages}\") msgs = self.serialize_messages(messages) py"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":421,"endLine":421}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: =response)) logger.debug(f\"Chat completion finished: {messages}\") return messages"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":435,"endLine":435}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: difflib import json import logging import os import platform import subprocess import sys from pathlib import Path import openai import ty"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/main.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":30,"endLine":30}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-automatic-logs","level":"warning","message":{"text":"Automatic recording of events over the lifetime — verdict: PARTIAL (62% conf).\n\nComposite raw score 0.48 (1/3 rules matched).\n\nLLM judge (confidence 0.62): The system demonstrates logging at tool call boundaries (5 evidence points in ai.py showing logger.debug calls around chat completion events) and imports the logging module, but lacks evidence of structured logging framework and persistent storage sink configuration. Automatic recording occurs at critical points but completeness across system lifetime and durability guarantees remain unverified.\n\nEvidence: ations import json import logging import os from pathlib import Path from typing import Any, List, Optional, Union import backoff import"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":19,"endLine":19}}}],"properties":{"verdict":"partial","score":2,"confidence":0.62,"rawScore":0.48000000000000004,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-12/p1-automatic-logs"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: difflib import json import logging import os import platform import subprocess import sys from pathlib import Path import openai import ty"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/main.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":30,"endLine":30}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: ations import json import logging import os from pathlib import Path from typing import Any, List, Optional, Union import backoff import"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":19,"endLine":19}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: odel_name) logger.debug(f\"Using model {self.model_name}\") def start(self, system: str, user: Any, *, step_name: str) -> List[M"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":118,"endLine":118}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: t=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\".join([m.pretty_repr() for m in messages"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":235,"endLine":235}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: d(response) logger.debug(f\"Chat completion finished: {messages}\") return messages @backoff.on_exception(backoff.expo,"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":249,"endLine":249}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: t=prompt)) logger.debug(f\"Creating a new chat completion: {messages}\") msgs = self.serialize_messages(messages) py"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":421,"endLine":421}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: difflib import json import logging import os import platform import subprocess import sys from pathlib import Path import openai import ty"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/main.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":30,"endLine":30}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/p2-traceability","level":"warning","message":{"text":"Logging ensures traceability appropriate to risk — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.57 (2/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: ations import json import logging import os from pathlib import Path from typing import Any, List, Optional, Union import backoff import"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":19,"endLine":19}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.575,"verifyMethod":"skipped","clauseId":"eu-ai-act/art-12/p2-traceability"}},{"ruleId":"eu-ai-act-2024-08/deployer-instructions","level":"error","message":{"text":"Transparent operation and instructions for use — verdict: FAIL (78% conf).\n\nComposite raw score 0.13 (0/3 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: 1 required sections present"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":0,"confidence":0.775,"rawScore":0.125,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-13/deployer-instructions"}},{"ruleId":"eu-ai-act-2024-08/deployer-instructions","level":"error","message":{"text":"Transparent operation and instructions for use — verdict: FAIL (78% conf).\n\nComposite raw score 0.13 (0/3 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":0,"confidence":0.775,"rawScore":0.125,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-13/deployer-instructions"}},{"ruleId":"eu-ai-act-2024-08/deployer-instructions","level":"error","message":{"text":"Transparent operation and instructions for use — verdict: FAIL (78% conf).\n\nComposite raw score 0.13 (0/3 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":0,"confidence":0.775,"rawScore":0.125,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-13/deployer-instructions"}},{"ruleId":"eu-ai-act-2024-08/p1-oversight-measures","level":"error","message":{"text":"Effective human oversight designed and built-in — verdict: FAIL (85% conf).\n\nComposite raw score 0.45 (1/3 rules matched).\n\nLLM judge (confidence 0.85): While human-machine message handling is present (HumanMessage/AIMessage structures), the evidence shows no actual oversight UI, dry-run capabilities, or documented mechanisms for humans to effectively intervene during system operation. Message passing alone is insufficient for 'effective oversight' as required by Art. 14(1); the system lacks the control interface tools necessary for active human supervision.\n\nEvidence: AIMessage, HumanMessage, SystemMessage, messages_from_dict, messages_to_dict, ) from langchain_anthropic import ChatAnt"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":33,"endLine":33}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.4460897842756548,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p1-oversight-measures"}},{"ruleId":"eu-ai-act-2024-08/p1-oversight-measures","level":"error","message":{"text":"Effective human oversight designed and built-in — verdict: FAIL (85% conf).\n\nComposite raw score 0.45 (1/3 rules matched).\n\nLLM judge (confidence 0.85): While human-machine message handling is present (HumanMessage/AIMessage structures), the evidence shows no actual oversight UI, dry-run capabilities, or documented mechanisms for humans to effectively intervene during system operation. Message passing alone is insufficient for 'effective oversight' as required by Art. 14(1); the system lacks the control interface tools necessary for active human supervision.\n\nEvidence: = Union[AIMessage, HumanMessage, SystemMessage] # Set up logging logger = logging.getLogger(__name__) class AI: \"\"\" A class that"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":44,"endLine":44}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.4460897842756548,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p1-oversight-measures"}},{"ruleId":"eu-ai-act-2024-08/p1-oversight-measures","level":"error","message":{"text":"Effective human oversight designed and built-in — verdict: FAIL (85% conf).\n\nComposite raw score 0.45 (1/3 rules matched).\n\nLLM judge (confidence 0.85): While human-machine message handling is present (HumanMessage/AIMessage structures), the evidence shows no actual oversight UI, dry-run capabilities, or documented mechanisms for humans to effectively intervene during system operation. Message passing alone is insufficient for 'effective oversight' as required by Art. 14(1); the system lacks the control interface tools necessary for active human supervision.\n\nEvidence: ystem), HumanMessage(content=user), ] return self.next(messages, step_name=step_name) def _extract_content("},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":141,"endLine":141}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.4460897842756548,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p1-oversight-measures"}},{"ruleId":"eu-ai-act-2024-08/p1-oversight-measures","level":"error","message":{"text":"Effective human oversight designed and built-in — verdict: FAIL (85% conf).\n\nComposite raw score 0.45 (1/3 rules matched).\n\nLLM judge (confidence 0.85): While human-machine message handling is present (HumanMessage/AIMessage structures), the evidence shows no actual oversight UI, dry-run capabilities, or documented mechanisms for humans to effectively intervene during system operation. Message passing alone is insufficient for 'effective oversight' as required by Art. 14(1); the system lacks the control interface tools necessary for active human supervision.\n\nEvidence: messages.append(HumanMessage(content=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\"."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":233,"endLine":233}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.4460897842756548,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p1-oversight-measures"}},{"ruleId":"eu-ai-act-2024-08/p1-oversight-measures","level":"error","message":{"text":"Effective human oversight designed and built-in — verdict: FAIL (85% conf).\n\nComposite raw score 0.45 (1/3 rules matched).\n\nLLM judge (confidence 0.85): While human-machine message handling is present (HumanMessage/AIMessage structures), the evidence shows no actual oversight UI, dry-run capabilities, or documented mechanisms for humans to effectively intervene during system operation. Message passing alone is insufficient for 'effective oversight' as required by Art. 14(1); the system lacks the control interface tools necessary for active human supervision.\n\nEvidence: e(content=\"Hello\"), HumanMessage(content=\"How's the weather?\")] >>> response = backoff_inference(messages) \"\"\" retur"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":284,"endLine":284}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.4460897842756548,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p1-oversight-measures"}},{"ruleId":"eu-ai-act-2024-08/p1-oversight-measures","level":"error","message":{"text":"Effective human oversight designed and built-in — verdict: FAIL (85% conf).\n\nComposite raw score 0.45 (1/3 rules matched).\n\nLLM judge (confidence 0.85): While human-machine message handling is present (HumanMessage/AIMessage structures), the evidence shows no actual oversight UI, dry-run capabilities, or documented mechanisms for humans to effectively intervene during system operation. Message passing alone is insufficient for 'effective oversight' as required by Art. 14(1); the system lacks the control interface tools necessary for active human supervision.\n\nEvidence: chain.schema import HumanMessage, SystemMessage from termcolor import colored from gpt_engineer.core.ai import AI from gpt_engineer.core.ba"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/default/steps.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":42,"endLine":42}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.4460897842756548,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p1-oversight-measures"}},{"ruleId":"eu-ai-act-2024-08/p4e-override","level":"error","message":{"text":"Ability to override / reverse the system's output — verdict: FAIL (85% conf).\n\nComposite raw score 0.60 (1/2 rules matched).\n\nLLM judge (confidence 0.85): Evidence shows human-in-loop message passing infrastructure (override_path_present matched), but repository lacks concrete implementation of decision reversal mechanisms or UI/API endpoints enabling humans to actually override or reverse AI outputs. The matched rule alone (0.6 weight) is insufficient without addressable decision structures (0.4 weight) required by Article 14(4)(e).\n\nEvidence: AIMessage, HumanMessage, SystemMessage, messages_from_dict, messages_to_dict, ) from langchain_anthropic import ChatAnt"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":33,"endLine":33}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.6,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p4e-override"}},{"ruleId":"eu-ai-act-2024-08/p4e-override","level":"error","message":{"text":"Ability to override / reverse the system's output — verdict: FAIL (85% conf).\n\nComposite raw score 0.60 (1/2 rules matched).\n\nLLM judge (confidence 0.85): Evidence shows human-in-loop message passing infrastructure (override_path_present matched), but repository lacks concrete implementation of decision reversal mechanisms or UI/API endpoints enabling humans to actually override or reverse AI outputs. The matched rule alone (0.6 weight) is insufficient without addressable decision structures (0.4 weight) required by Article 14(4)(e).\n\nEvidence: = Union[AIMessage, HumanMessage, SystemMessage] # Set up logging logger = logging.getLogger(__name__) class AI: \"\"\" A class that"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":44,"endLine":44}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.6,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p4e-override"}},{"ruleId":"eu-ai-act-2024-08/p4e-override","level":"error","message":{"text":"Ability to override / reverse the system's output — verdict: FAIL (85% conf).\n\nComposite raw score 0.60 (1/2 rules matched).\n\nLLM judge (confidence 0.85): Evidence shows human-in-loop message passing infrastructure (override_path_present matched), but repository lacks concrete implementation of decision reversal mechanisms or UI/API endpoints enabling humans to actually override or reverse AI outputs. The matched rule alone (0.6 weight) is insufficient without addressable decision structures (0.4 weight) required by Article 14(4)(e).\n\nEvidence: ystem), HumanMessage(content=user), ] return self.next(messages, step_name=step_name) def _extract_content("},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":141,"endLine":141}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.6,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p4e-override"}},{"ruleId":"eu-ai-act-2024-08/p4e-override","level":"error","message":{"text":"Ability to override / reverse the system's output — verdict: FAIL (85% conf).\n\nComposite raw score 0.60 (1/2 rules matched).\n\nLLM judge (confidence 0.85): Evidence shows human-in-loop message passing infrastructure (override_path_present matched), but repository lacks concrete implementation of decision reversal mechanisms or UI/API endpoints enabling humans to actually override or reverse AI outputs. The matched rule alone (0.6 weight) is insufficient without addressable decision structures (0.4 weight) required by Article 14(4)(e).\n\nEvidence: messages.append(HumanMessage(content=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\"."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":233,"endLine":233}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.6,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p4e-override"}},{"ruleId":"eu-ai-act-2024-08/p4e-override","level":"error","message":{"text":"Ability to override / reverse the system's output — verdict: FAIL (85% conf).\n\nComposite raw score 0.60 (1/2 rules matched).\n\nLLM judge (confidence 0.85): Evidence shows human-in-loop message passing infrastructure (override_path_present matched), but repository lacks concrete implementation of decision reversal mechanisms or UI/API endpoints enabling humans to actually override or reverse AI outputs. The matched rule alone (0.6 weight) is insufficient without addressable decision structures (0.4 weight) required by Article 14(4)(e).\n\nEvidence: e(content=\"Hello\"), HumanMessage(content=\"How's the weather?\")] >>> response = backoff_inference(messages) \"\"\" retur"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":284,"endLine":284}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.6,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p4e-override"}},{"ruleId":"eu-ai-act-2024-08/p4e-override","level":"error","message":{"text":"Ability to override / reverse the system's output — verdict: FAIL (85% conf).\n\nComposite raw score 0.60 (1/2 rules matched).\n\nLLM judge (confidence 0.85): Evidence shows human-in-loop message passing infrastructure (override_path_present matched), but repository lacks concrete implementation of decision reversal mechanisms or UI/API endpoints enabling humans to actually override or reverse AI outputs. The matched rule alone (0.6 weight) is insufficient without addressable decision structures (0.4 weight) required by Article 14(4)(e).\n\nEvidence: chain.schema import HumanMessage, SystemMessage from termcolor import colored from gpt_engineer.core.ai import AI from gpt_engineer.core.ba"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/default/steps.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":42,"endLine":42}}}],"properties":{"verdict":"fail","score":0,"confidence":0.85,"rawScore":0.6,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-14/p4e-override"}},{"ruleId":"eu-ai-act-2024-08/p1-accuracy","level":"warning","message":{"text":"Appropriate level of accuracy declared and tested — verdict: PARTIAL (72% conf).\n\nComposite raw score 0.70 (2/3 rules matched).\n\nLLM judge (confidence 0.72): The system demonstrates evaluation infrastructure (eval_suite_present, eval_in_ci) but lacks documented metrics specifications required by Article 15(1). Without explicit accuracy, robustness, and cybersecurity metrics documentation, compliance with 'appropriate level' and 'consistent performance' requirements cannot be fully verified."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"evals/","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.72,"rawScore":0.7,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-15/p1-accuracy"}},{"ruleId":"eu-ai-act-2024-08/p1-accuracy","level":"warning","message":{"text":"Appropriate level of accuracy declared and tested — verdict: PARTIAL (72% conf).\n\nComposite raw score 0.70 (2/3 rules matched).\n\nLLM judge (confidence 0.72): The system demonstrates evaluation infrastructure (eval_suite_present, eval_in_ci) but lacks documented metrics specifications required by Article 15(1). Without explicit accuracy, robustness, and cybersecurity metrics documentation, compliance with 'appropriate level' and 'consistent performance' requirements cannot be fully verified."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.72,"rawScore":0.7,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-15/p1-accuracy"}},{"ruleId":"eu-ai-act-2024-08/p1-accuracy","level":"warning","message":{"text":"Appropriate level of accuracy declared and tested — verdict: PARTIAL (72% conf).\n\nComposite raw score 0.70 (2/3 rules matched).\n\nLLM judge (confidence 0.72): The system demonstrates evaluation infrastructure (eval_suite_present, eval_in_ci) but lacks documented metrics specifications required by Article 15(1). Without explicit accuracy, robustness, and cybersecurity metrics documentation, compliance with 'appropriate level' and 'consistent performance' requirements cannot be fully verified."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/workflows/ci.yaml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.72,"rawScore":0.7,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-15/p1-accuracy"}},{"ruleId":"eu-ai-act-2024-08/p4-robustness","level":"error","message":{"text":"Resilience to errors, faults, inconsistencies — verdict: FAIL (81% conf).\n\nComposite raw score 0.16 (0/3 rules matched).\n\nEvidence: LangChain"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"pyproject.toml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":1,"confidence":0.81,"rawScore":0.16000000000000003,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-15/p4-robustness"}},{"ruleId":"eu-ai-act-2024-08/p4-robustness","level":"error","message":{"text":"Resilience to errors, faults, inconsistencies — verdict: FAIL (81% conf).\n\nComposite raw score 0.16 (0/3 rules matched).\n\nEvidence: Anthropic SDK"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"pyproject.toml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":1,"confidence":0.81,"rawScore":0.16000000000000003,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-15/p4-robustness"}},{"ruleId":"eu-ai-act-2024-08/p5-cybersecurity","level":"error","message":{"text":"Cybersecurity measures appropriate to circumstances — verdict: FAIL (82% conf).\n\nComposite raw score 0.12 (1/4 rules matched).\n\nEvidence: ackoff.expo, openai.RateLimitError, max_tries=7, max_time=45) def backoff_inference(self, messages): \"\"\" Perform inferen"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":253,"endLine":253}}}],"properties":{"verdict":"fail","score":0,"confidence":0.818169700377573,"rawScore":0.118169700377573,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-15/p5-cybersecurity"}},{"ruleId":"eu-ai-act-2024-08/p5-cybersecurity","level":"error","message":{"text":"Cybersecurity measures appropriate to circumstances — verdict: FAIL (82% conf).\n\nComposite raw score 0.12 (1/4 rules matched).\n\nEvidence: openai.error.RateLimitError If the number of retries exceeds the maximum or if the rate limit persists beyond the"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":278,"endLine":278}}}],"properties":{"verdict":"fail","score":0,"confidence":0.818169700377573,"rawScore":0.118169700377573,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-15/p5-cybersecurity"}},{"ruleId":"eu-ai-act-2024-08/p5-cybersecurity","level":"error","message":{"text":"Cybersecurity measures appropriate to circumstances — verdict: FAIL (82% conf).\n\nComposite raw score 0.12 (1/4 rules matched).\n\nEvidence: ultimately raise a RateLimitError. Example ------- >>> messages = [SystemMessage(content=\"Hello\"), HumanMessage(co"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":280,"endLine":280}}}],"properties":{"verdict":"fail","score":0,"confidence":0.818169700377573,"rawScore":0.118169700377573,"verifyMethod":"deterministic-only","clauseId":"eu-ai-act/art-15/p5-cybersecurity"}},{"ruleId":"eu-ai-act-2024-08/p6-keep-logs","level":"warning","message":{"text":"Deployer log-retention capability supported — verdict: PARTIAL (35% conf).\n\nComposite raw score 0.50 (1/1 rules matched). Supporting docs may exist outside the repo.\n\nLLM judge (confidence 0.35): Logging imports are present, confirming technical capability for log generation, but evidence does not demonstrate: (1) automatic log generation by the AI system itself, (2) logs under deployer control, (3) retention policies meeting the six-month minimum, or (4) exported logs accessibility. Deterministic rule only confirms exportability potential; actual compliance requires documentation of retention procedures and log access mechanisms.\n\nEvidence: difflib import json import logging import os import platform import subprocess import sys from pathlib import Path import openai import ty"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/main.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":30,"endLine":30}}}],"properties":{"verdict":"partial","score":2,"confidence":0.35,"rawScore":0.5,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-26/p6-keep-logs"}},{"ruleId":"eu-ai-act-2024-08/p6-keep-logs","level":"warning","message":{"text":"Deployer log-retention capability supported — verdict: PARTIAL (35% conf).\n\nComposite raw score 0.50 (1/1 rules matched). Supporting docs may exist outside the repo.\n\nLLM judge (confidence 0.35): Logging imports are present, confirming technical capability for log generation, but evidence does not demonstrate: (1) automatic log generation by the AI system itself, (2) logs under deployer control, (3) retention policies meeting the six-month minimum, or (4) exported logs accessibility. Deterministic rule only confirms exportability potential; actual compliance requires documentation of retention procedures and log access mechanisms.\n\nEvidence: ations import json import logging import os from pathlib import Path from typing import Any, List, Optional, Union import backoff import"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":19,"endLine":19}}}],"properties":{"verdict":"partial","score":2,"confidence":0.35,"rawScore":0.5,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-26/p6-keep-logs"}},{"ruleId":"eu-ai-act-2024-08/p1-ai-disclosure","level":"warning","message":{"text":"Users informed they are interacting with an AI — verdict: PARTIAL (45% conf).\n\nComposite raw score 0.40 (1/2 rules matched).\n\nLLM judge (confidence 0.45): The system fails explicit disclosure in user-facing strings (README.md) but demonstrates non-human persona design in core prompts, creating ambiguity about whether a reasonably informed user would recognize AI interaction. Human judgment needed on whether prompt-level design choices substitute for explicit disclosure statements."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.45,"rawScore":0.4,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-50/p1-ai-disclosure"}},{"ruleId":"eu-ai-act-2024-08/p1-ai-disclosure","level":"warning","message":{"text":"Users informed they are interacting with an AI — verdict: PARTIAL (45% conf).\n\nComposite raw score 0.40 (1/2 rules matched).\n\nLLM judge (confidence 0.45): The system fails explicit disclosure in user-facing strings (README.md) but demonstrates non-human persona design in core prompts, creating ambiguity about whether a reasonably informed user would recognize AI interaction. Human judgment needed on whether prompt-level design choices substitute for explicit disclosure statements."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/prompt.py","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.45,"rawScore":0.4,"verifyMethod":"llm-judge","clauseId":"eu-ai-act/art-50/p1-ai-disclosure"}},{"ruleId":"nist-ai-rmf-1.0/govern-1.5","level":"warning","message":{"text":"Ongoing monitoring and periodic review of risk management — verdict: PARTIAL (45% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo.\n\nLLM judge (confidence 0.45): CI/CD evaluation gates demonstrate some monitoring capability, but the absence of documented drift monitoring and lack of evidence for defined organizational roles, responsibilities, and periodic review frequency prevents full compliance with GOVERN 1.5's comprehensive requirements."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/workflows/ci.yaml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.45,"rawScore":0.5,"verifyMethod":"llm-judge","clauseId":"nist-ai-rmf/govern-1.5"}},{"ruleId":"nist-ai-rmf-1.0/map-1.1","level":"error","message":{"text":"Context of use established and understood — verdict: FAIL (75% conf).\n\nComposite raw score 0.15 (0/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: 1 required sections present"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":1,"confidence":0.75,"rawScore":0.15,"verifyMethod":"deterministic-only","clauseId":"nist-ai-rmf/map-1.1"}},{"ruleId":"nist-ai-rmf-1.0/map-1.1","level":"error","message":{"text":"Context of use established and understood — verdict: FAIL (75% conf).\n\nComposite raw score 0.15 (0/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"fail","score":1,"confidence":0.75,"rawScore":0.15,"verifyMethod":"deterministic-only","clauseId":"nist-ai-rmf/map-1.1"}},{"ruleId":"nist-ai-rmf-1.0/measure-2.3","level":"warning","message":{"text":"AI system performance evaluated and documented — verdict: PARTIAL (72% conf).\n\nComposite raw score 0.70 (2/3 rules matched).\n\nLLM judge (confidence 0.72): Evaluation suite and CI integration are present (0.7 raw score), but absence of documented metrics is a material gap for demonstrating measured performance criteria as required by the clause. Documentation of measures is explicitly mandated and currently missing."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"evals/","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.72,"rawScore":0.7,"verifyMethod":"llm-judge","clauseId":"nist-ai-rmf/measure-2.3"}},{"ruleId":"nist-ai-rmf-1.0/measure-2.3","level":"warning","message":{"text":"AI system performance evaluated and documented — verdict: PARTIAL (72% conf).\n\nComposite raw score 0.70 (2/3 rules matched).\n\nLLM judge (confidence 0.72): Evaluation suite and CI integration are present (0.7 raw score), but absence of documented metrics is a material gap for demonstrating measured performance criteria as required by the clause. Documentation of measures is explicitly mandated and currently missing."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.72,"rawScore":0.7,"verifyMethod":"llm-judge","clauseId":"nist-ai-rmf/measure-2.3"}},{"ruleId":"nist-ai-rmf-1.0/measure-2.3","level":"warning","message":{"text":"AI system performance evaluated and documented — verdict: PARTIAL (72% conf).\n\nComposite raw score 0.70 (2/3 rules matched).\n\nLLM judge (confidence 0.72): Evaluation suite and CI integration are present (0.7 raw score), but absence of documented metrics is a material gap for demonstrating measured performance criteria as required by the clause. Documentation of measures is explicitly mandated and currently missing."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/workflows/ci.yaml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":0.72,"rawScore":0.7,"verifyMethod":"llm-judge","clauseId":"nist-ai-rmf/measure-2.3"}},{"ruleId":"nist-ai-rmf-1.0/measure-2.7","level":"error","message":{"text":"Security and resilience evaluated — verdict: FAIL (77% conf).\n\nComposite raw score 0.12 (1/3 rules matched).\n\nEvidence: ackoff.expo, openai.RateLimitError, max_tries=7, max_time=45) def backoff_inference(self, messages): \"\"\" Perform inferen"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":253,"endLine":253}}}],"properties":{"verdict":"fail","score":0,"confidence":0.768169700377573,"rawScore":0.118169700377573,"verifyMethod":"deterministic-only","clauseId":"nist-ai-rmf/measure-2.7"}},{"ruleId":"nist-ai-rmf-1.0/measure-2.7","level":"error","message":{"text":"Security and resilience evaluated — verdict: FAIL (77% conf).\n\nComposite raw score 0.12 (1/3 rules matched).\n\nEvidence: openai.error.RateLimitError If the number of retries exceeds the maximum or if the rate limit persists beyond the"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":278,"endLine":278}}}],"properties":{"verdict":"fail","score":0,"confidence":0.768169700377573,"rawScore":0.118169700377573,"verifyMethod":"deterministic-only","clauseId":"nist-ai-rmf/measure-2.7"}},{"ruleId":"nist-ai-rmf-1.0/measure-2.7","level":"error","message":{"text":"Security and resilience evaluated — verdict: FAIL (77% conf).\n\nComposite raw score 0.12 (1/3 rules matched).\n\nEvidence: ultimately raise a RateLimitError. Example ------- >>> messages = [SystemMessage(content=\"Hello\"), HumanMessage(co"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":280,"endLine":280}}}],"properties":{"verdict":"fail","score":0,"confidence":0.768169700377573,"rawScore":0.118169700377573,"verifyMethod":"deterministic-only","clauseId":"nist-ai-rmf/measure-2.7"}},{"ruleId":"nist-ai-rmf-1.0/manage-4.1","level":"error","message":{"text":"Post-deployment monitoring, appeal and override, change management — verdict: FAIL (100% conf).\n\nComposite raw score 0.36 (1/4 rules matched).\n\nEvidence: AIMessage, HumanMessage, SystemMessage, messages_from_dict, messages_to_dict, ) from langchain_anthropic import ChatAnt"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":33,"endLine":33}}}],"properties":{"verdict":"fail","score":1,"confidence":1,"rawScore":0.36,"verifyMethod":"skipped","clauseId":"nist-ai-rmf/manage-4.1"}},{"ruleId":"nist-ai-rmf-1.0/manage-4.1","level":"error","message":{"text":"Post-deployment monitoring, appeal and override, change management — verdict: FAIL (100% conf).\n\nComposite raw score 0.36 (1/4 rules matched).\n\nEvidence: = Union[AIMessage, HumanMessage, SystemMessage] # Set up logging logger = logging.getLogger(__name__) class AI: \"\"\" A class that"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":44,"endLine":44}}}],"properties":{"verdict":"fail","score":1,"confidence":1,"rawScore":0.36,"verifyMethod":"skipped","clauseId":"nist-ai-rmf/manage-4.1"}},{"ruleId":"nist-ai-rmf-1.0/manage-4.1","level":"error","message":{"text":"Post-deployment monitoring, appeal and override, change management — verdict: FAIL (100% conf).\n\nComposite raw score 0.36 (1/4 rules matched).\n\nEvidence: ystem), HumanMessage(content=user), ] return self.next(messages, step_name=step_name) def _extract_content("},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":141,"endLine":141}}}],"properties":{"verdict":"fail","score":1,"confidence":1,"rawScore":0.36,"verifyMethod":"skipped","clauseId":"nist-ai-rmf/manage-4.1"}},{"ruleId":"nist-ai-rmf-1.0/manage-4.1","level":"error","message":{"text":"Post-deployment monitoring, appeal and override, change management — verdict: FAIL (100% conf).\n\nComposite raw score 0.36 (1/4 rules matched).\n\nEvidence: messages.append(HumanMessage(content=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\"."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":233,"endLine":233}}}],"properties":{"verdict":"fail","score":1,"confidence":1,"rawScore":0.36,"verifyMethod":"skipped","clauseId":"nist-ai-rmf/manage-4.1"}},{"ruleId":"nist-ai-rmf-1.0/manage-4.1","level":"error","message":{"text":"Post-deployment monitoring, appeal and override, change management — verdict: FAIL (100% conf).\n\nComposite raw score 0.36 (1/4 rules matched).\n\nEvidence: e(content=\"Hello\"), HumanMessage(content=\"How's the weather?\")] >>> response = backoff_inference(messages) \"\"\" retur"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":284,"endLine":284}}}],"properties":{"verdict":"fail","score":1,"confidence":1,"rawScore":0.36,"verifyMethod":"skipped","clauseId":"nist-ai-rmf/manage-4.1"}},{"ruleId":"nist-ai-rmf-1.0/manage-4.1","level":"error","message":{"text":"Post-deployment monitoring, appeal and override, change management — verdict: FAIL (100% conf).\n\nComposite raw score 0.36 (1/4 rules matched).\n\nEvidence: chain.schema import HumanMessage, SystemMessage from termcolor import colored from gpt_engineer.core.ai import AI from gpt_engineer.core.ba"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/default/steps.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":42,"endLine":42}}}],"properties":{"verdict":"fail","score":1,"confidence":1,"rawScore":0.36,"verifyMethod":"skipped","clauseId":"nist-ai-rmf/manage-4.1"}},{"ruleId":"iso-42001-2023/clause-5.3-roles","level":"note","message":{"text":"Roles, responsibilities and authorities — verdict: PASS (55% conf).\n\nComposite raw score 1.00 (1/1 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/CODEOWNERS","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"pass","score":4,"confidence":0.55,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"iso-42001/clause-5.3-roles"}},{"ruleId":"iso-42001-2023/clause-7.5-documented-information","level":"note","message":{"text":"Documented information for the AI management system — verdict: PASS (90% conf).\n\nComposite raw score 0.70 (1/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"pass","score":3,"confidence":0.9,"rawScore":0.7,"verifyMethod":"skipped","clauseId":"iso-42001/clause-7.5-documented-information"}},{"ruleId":"iso-42001-2023/clause-7.5-documented-information","level":"note","message":{"text":"Documented information for the AI management system — verdict: PASS (90% conf).\n\nComposite raw score 0.70 (1/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/workflows/release.yaml","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"pass","score":3,"confidence":0.9,"rawScore":0.7,"verifyMethod":"skipped","clauseId":"iso-42001/clause-7.5-documented-information"}},{"ruleId":"iso-42001-2023/clause-8.1-operational-planning","level":"warning","message":{"text":"Operational planning and control — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/workflows/","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.5,"verifyMethod":"skipped","clauseId":"iso-42001/clause-8.1-operational-planning"}},{"ruleId":"iso-42001-2023/clause-9.1-monitoring","level":"note","message":{"text":"Monitoring, measurement, analysis and evaluation — verdict: PASS (85% conf).\n\nComposite raw score 0.75 (2/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"evals/","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"pass","score":3,"confidence":0.85,"rawScore":0.75,"verifyMethod":"deterministic-only","clauseId":"iso-42001/clause-9.1-monitoring"}},{"ruleId":"iso-42001-2023/clause-9.1-monitoring","level":"note","message":{"text":"Monitoring, measurement, analysis and evaluation — verdict: PASS (85% conf).\n\nComposite raw score 0.75 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: difflib import json import logging import os import platform import subprocess import sys from pathlib import Path import openai import ty"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/main.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":30,"endLine":30}}}],"properties":{"verdict":"pass","score":3,"confidence":0.85,"rawScore":0.75,"verifyMethod":"deterministic-only","clauseId":"iso-42001/clause-9.1-monitoring"}},{"ruleId":"iso-42001-2023/clause-9.1-monitoring","level":"note","message":{"text":"Monitoring, measurement, analysis and evaluation — verdict: PASS (85% conf).\n\nComposite raw score 0.75 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: ations import json import logging import os from pathlib import Path from typing import Any, List, Optional, Union import backoff import"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":19,"endLine":19}}}],"properties":{"verdict":"pass","score":3,"confidence":0.85,"rawScore":0.75,"verifyMethod":"deterministic-only","clauseId":"iso-42001/clause-9.1-monitoring"}},{"ruleId":"iso-42001-2023/annex-a5-internal-org","level":"note","message":{"text":"Internal organization controls — verdict: PASS (55% conf).\n\nComposite raw score 1.00 (1/1 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":".github/CODEOWNERS","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"pass","score":4,"confidence":0.55,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"iso-42001/annex-a5-internal-org"}},{"ruleId":"iso-42001-2023/annex-a7-resources","level":"warning","message":{"text":"Resources for AI systems — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"poetry.lock","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.5,"verifyMethod":"skipped","clauseId":"iso-42001/annex-a7-resources"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: AIMessage, HumanMessage, SystemMessage, messages_from_dict, messages_to_dict, ) from langchain_anthropic import ChatAnt"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":33,"endLine":33}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: = Union[AIMessage, HumanMessage, SystemMessage] # Set up logging logger = logging.getLogger(__name__) class AI: \"\"\" A class that"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":44,"endLine":44}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: ystem), HumanMessage(content=user), ] return self.next(messages, step_name=step_name) def _extract_content("},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":141,"endLine":141}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: messages.append(HumanMessage(content=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\"."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":233,"endLine":233}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: e(content=\"Hello\"), HumanMessage(content=\"How's the weather?\")] >>> response = backoff_inference(messages) \"\"\" retur"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":284,"endLine":284}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: chain.schema import HumanMessage, SystemMessage from termcolor import colored from gpt_engineer.core.ai import AI from gpt_engineer.core.ba"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/default/steps.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":42,"endLine":42}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: AIMessage, HumanMessage, SystemMessage, messages_from_dict, messages_to_dict, ) from langchain_anthropic import ChatAnt"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":33,"endLine":33}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: = Union[AIMessage, HumanMessage, SystemMessage] # Set up logging logger = logging.getLogger(__name__) class AI: \"\"\" A class that"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":44,"endLine":44}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: ystem), HumanMessage(content=user), ] return self.next(messages, step_name=step_name) def _extract_content("},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":141,"endLine":141}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: messages.append(HumanMessage(content=prompt)) logger.debug( \"Creating a new chat completion: %s\", \"\\n\"."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":233,"endLine":233}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: e(content=\"Hello\"), HumanMessage(content=\"How's the weather?\")] >>> response = backoff_inference(messages) \"\"\" retur"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/ai.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":284,"endLine":284}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-22-automated-decisions","level":"note","message":{"text":"Automated individual decision-making, including profiling — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: chain.schema import HumanMessage, SystemMessage from termcolor import colored from gpt_engineer.core.ai import AI from gpt_engineer.core.ba"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/default/steps.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":42,"endLine":42}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-22-automated-decisions"}},{"ruleId":"gdpr-2016/art-25-by-design","level":"note","message":{"text":"Data protection by design and by default — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: hashlib.sha256"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/collect.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":138,"endLine":138}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-25-by-design"}},{"ruleId":"gdpr-2016/art-25-by-design","level":"note","message":{"text":"Data protection by design and by default — verdict: PASS (60% conf).\n\nComposite raw score 1.00 (2/2 rules matched). Supporting docs may exist outside the repo."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"README.md","uriBaseId":"%SRCROOT%"}}}],"properties":{"verdict":"pass","score":4,"confidence":0.6,"rawScore":1,"verifyMethod":"deterministic-only","clauseId":"gdpr/art-25-by-design"}},{"ruleId":"gdpr-2016/art-32-security","level":"warning","message":{"text":"Security of processing — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: https://gptengineerezm.dataplane.rudderstack.com"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/collect.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":56,"endLine":56}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.5,"verifyMethod":"skipped","clauseId":"gdpr/art-32-security"}},{"ruleId":"gdpr-2016/art-32-security","level":"warning","message":{"text":"Security of processing — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: https://xx.openai.azure.com)."},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/applications/cli/main.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":320,"endLine":320}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.5,"verifyMethod":"skipped","clauseId":"gdpr/art-32-security"}},{"ruleId":"gdpr-2016/art-32-security","level":"warning","message":{"text":"Security of processing — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: https://api.gptengineer.app/openapi.json"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/project_config.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":28,"endLine":28}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.5,"verifyMethod":"skipped","clauseId":"gdpr/art-32-security"}},{"ruleId":"gdpr-2016/art-32-security","level":"warning","message":{"text":"Security of processing — verdict: PARTIAL (100% conf).\n\nComposite raw score 0.50 (1/2 rules matched). Supporting docs may exist outside the repo.\n\nEvidence: https://github.com/langchain-ai/langchain/blob/535db72607c4ae308566ede4af65295967bb33a8/libs/community/langchain_community/callbacks/openai_info.py"},"locations":[{"physicalLocation":{"artifactLocation":{"uri":"gpt_engineer/core/token_usage.py","uriBaseId":"%SRCROOT%"},"region":{"startLine":15,"endLine":15}}}],"properties":{"verdict":"partial","score":2,"confidence":1,"rawScore":0.5,"verifyMethod":"skipped","clauseId":"gdpr/art-32-security"}}],"properties":{"auditId":"aud_01KS70FB8DCPW4MD6ZHCSK","bundleHash":"28ffdb8667786fb772e3cb282cb5e7315194c72b6eefafec1725710d921d3439","durationMs":33391,"overallScore":1.6666666666666667,"regulationCoverage":["eu-ai-act-2024-08","nist-ai-rmf-1.0","iso-42001-2023","gdpr-2016"]}}]}