{"version": "2.1.0", "$schema": "https://json.schemastore.org/sarif-2.1.0.json", "runs": [{"tool": {"driver": {"name": "Repobility", "informationUri": "https://repobility.com", "rules": [{"id": "AIC001", "name": "Parallel implementation file sits beside a canonical file", "shortDescription": {"text": "Parallel implementation file sits beside a canonical file"}, "fullDescription": {"text": "Merge the intended change into the canonical file, update tests/imports, and delete the parallel implementation if it is not the active entry point."}, "properties": {"scanner": "repobility-ai-code-hygiene", "category": "quality", "severity": "medium", "confidence": 0.82, "cwe": "", "owasp": ""}}, {"id": "CFG006", "name": "[CFG006] Missing .gitignore: No .gitignore file. Risk of committing secrets and build artifacts.", "shortDescription": {"text": "[CFG006] Missing .gitignore: No .gitignore file. Risk of committing secrets and build artifacts."}, "fullDescription": {"text": "Add a .gitignore appropriate for your language/framework."}, "properties": {"scanner": "repobility-threat-engine", "category": "practices", "severity": "medium", "confidence": 1.0, "cwe": "", "owasp": ""}}, {"id": "SEC017", "name": "[SEC017] Unbounded Input to LLM/External API: User input is passed to an LLM or external AI API (OpenAI, Anthropic, etc.", "shortDescription": {"text": "[SEC017] Unbounded Input to LLM/External API: User input is passed to an LLM or external AI API (OpenAI, Anthropic, etc.) without any visible length or size validation. This creates two risks: (1) Cost abuse \u2014 an attacker can send extremely"}, "fullDescription": {"text": "1) Enforce a maximum input length BEFORE sending to the API: e.g. `if len(text) > 4000: return error`. 2) Use token counting (tiktoken for OpenAI, anthropic's token counter) to enforce token-level limits. 3) Set max_tokens on the API call to cap response cost. 4) Add rate limiting per user/IP to prevent automated abuse. 5) Monitor API spend with alerts for unusual usage patterns."}, "properties": {"scanner": "repobility-threat-engine", "category": "llm_injection", "severity": "medium", "confidence": 0.8, "cwe": "", "owasp": ""}}, {"id": "SEC034", "name": "[SEC034] Log Injection / Log Forging \u2014 unsanitized user input in log: User input is logged without sanitizing newlines o", "shortDescription": {"text": "[SEC034] Log Injection / Log Forging \u2014 unsanitized user input in log: User input is logged without sanitizing newlines or control characters. Attackers inject `\\n` to forge fake log entries, hide tracks, or exploit downstream log parsers (S"}, "fullDescription": {"text": "Strip control characters before logging:\n  safe = user_input.replace('\\n','').replace('\\r','').replace('\\x00','')\n  logger.info('User action: %s', safe)\nAlways use parameterized logging (`%s` + args), never f-strings or string concat \u2014 that's also what mitigates log4shell-style attacks. For structured logging, use a JSON formatter that escapes values."}, "properties": {"scanner": "repobility-threat-engine", "category": "log_injection", "severity": "medium", "confidence": 1.0, "cwe": "", "owasp": ""}}, {"id": "SEC015", "name": "[SEC015] Insecure Randomness for Security: Weak PRNG used in security-sensitive context. Output is predictable.", "shortDescription": {"text": "[SEC015] Insecure Randomness for Security: Weak PRNG used in security-sensitive context. Output is predictable."}, "fullDescription": {"text": "Use secrets module (Python) or crypto.getRandomValues() (JS) for security-sensitive randomness."}, "properties": {"scanner": "repobility-threat-engine", "category": "crypto", "severity": "medium", "confidence": 1.0, "cwe": "", "owasp": ""}}, {"id": "SEC007", "name": "[SEC007] Unsafe Deserialization: Unsafe deserialization can execute arbitrary code.", "shortDescription": {"text": "[SEC007] Unsafe Deserialization: Unsafe deserialization can execute arbitrary code."}, "fullDescription": {"text": "Use yaml.safe_load() instead of yaml.load(). Avoid pickle for untrusted data."}, "properties": {"scanner": "repobility-threat-engine", "category": "deserialization", "severity": "medium", "confidence": 1.0, "cwe": "", "owasp": ""}}, {"id": "SEC011", "name": "[SEC011] Unsafe PyTorch Model Loading: torch.load() uses pickle internally and can execute arbitrary code from untrusted", "shortDescription": {"text": "[SEC011] Unsafe PyTorch Model Loading: torch.load() uses pickle internally and can execute arbitrary code from untrusted model files."}, "fullDescription": {"text": "Use torch.load(..., weights_only=True) or use safetensors format."}, "properties": {"scanner": "repobility-threat-engine", "category": "deserialization", "severity": "medium", "confidence": 1.0, "cwe": "", "owasp": ""}}, {"id": "CORE_NO_CI", "name": "No CI/CD configuration found", "shortDescription": {"text": "No CI/CD configuration found"}, "fullDescription": {"text": "Add a CI/CD pipeline: create .github/workflows/ci.yml for GitHub Actions with steps to lint, test, and build on every push and pull request."}, "properties": {"scanner": "repobility-core", "category": "practices", "severity": "medium", "confidence": null, "cwe": "", "owasp": ""}}, {"id": "AIC005", "name": "Duplicate top-level symbol appears in a patch-style file", "shortDescription": {"text": "Duplicate top-level symbol appears in a patch-style file"}, "fullDescription": {"text": "Keep one authoritative implementation, update imports to point at it, and remove or rename the duplicate symbol."}, "properties": {"scanner": "repobility-ai-code-hygiene", "category": "quality", "severity": "low", "confidence": 0.64, "cwe": "", "owasp": ""}}, {"id": "AIC003", "name": "Duplicated implementation block across source files", "shortDescription": {"text": "Duplicated implementation block across source files"}, "fullDescription": {"text": "Extract the shared behavior into one function/module or delete the inactive duplicate after proving which path is used."}, "properties": {"scanner": "repobility-ai-code-hygiene", "category": "quality", "severity": "low", "confidence": 0.86, "cwe": "", "owasp": ""}}, {"id": "CORE_NO_TESTS", "name": "No test files found in a documentation, catalog, or template-heavy repository", "shortDescription": {"text": "No test files found in a documentation, catalog, or template-heavy repository"}, "fullDescription": {"text": "If this repository ships runnable code, add focused tests for those examples or templates. If it is documentation/catalog content only, mark the finding as accepted or add a .repobilityignore note."}, "properties": {"scanner": "repobility-core", "category": "testing", "severity": "info", "confidence": 0.35, "cwe": "", "owasp": ""}}, {"id": "SEC013", "name": "[SEC013] Path Traversal \u2014 User Input in File Path: User-controlled input used in file path without sanitization. Allows ", "shortDescription": {"text": "[SEC013] Path Traversal \u2014 User Input in File Path: User-controlled input used in file path without sanitization. Allows reading arbitrary files."}, "fullDescription": {"text": "Use os.path.realpath() and verify the path starts with your expected base directory. Use secure_filename() for uploads."}, "properties": {"scanner": "repobility-threat-engine", "category": "path_traversal", "severity": "high", "confidence": 0.8, "cwe": "", "owasp": ""}}, {"id": "SEC016", "name": "[SEC016] LLM Prompt Injection \u2014 User Input in AI Prompt: User-supplied text is interpolated directly into an AI/LLM prom", "shortDescription": {"text": "[SEC016] LLM Prompt Injection \u2014 User Input in AI Prompt: User-supplied text is interpolated directly into an AI/LLM prompt (e.g. OpenAI, Anthropic, or local model). This is the AI equivalent of SQL injection: an attacker can craft input tha"}, "fullDescription": {"text": "1) Separate user content from instructions: use the 'user' role for user text and 'system' role for your instructions \u2014 never concatenate them into one string. 2) Validate and constrain: limit input length, strip control characters, and reject known injection patterns. 3) Use structured output (JSON mode / function calling) so the model returns data, not freeform actions. 4) Apply output validation: check the AI's response before acting on it. 5) Consider a prompt injection detection layer (e.g. Anthropic's constitutional AI, prompt-guard models)."}, "properties": {"scanner": "repobility-threat-engine", "category": "llm_injection", "severity": "high", "confidence": 0.9, "cwe": "", "owasp": ""}}, {"id": "SEC004", "name": "[SEC004] SQL Injection Risk: String interpolation in SQL execution. Allows SQL injection.", "shortDescription": {"text": "[SEC004] SQL Injection Risk: String interpolation in SQL execution. Allows SQL injection."}, "fullDescription": {"text": "Use parameterized queries: cursor.execute('SELECT * FROM t WHERE id = %s', [id]). For dynamic table or column names, choose identifiers from a hard-coded allowlist and keep values in parameters."}, "properties": {"scanner": "repobility-threat-engine", "category": "injection", "severity": "high", "confidence": 0.85, "cwe": "", "owasp": ""}}]}}, "automationDetails": {"id": "repobility/240"}, "properties": {"repository": "https://github.com/google-research/google-research.git", "repoUrl": "https://github.com/google-research/google-research.git", "branch": "master"}, "results": [{"ruleId": "AIC001", "level": "warning", "message": {"text": "Parallel implementation file sits beside a canonical file"}, "properties": {"repobilityId": 23096, "scanner": "repobility-ai-code-hygiene", "fingerprint": "990476a2712aa5e541e4a411ad6371df0e5422df0cb3f0839afbc483e01eb748", "category": "quality", "severity": "medium", "confidence": 0.82, "triageState": "open", "verdict": "likely", "isResolved": false, "reason": "Source filename has a patch-style suffix and a same-directory canonical sibling exists.", "evidence": {"suffix": "v2", "rule_id": "AIC001", "scanner": "repobility-ai-code-hygiene", "references": ["https://arxiv.org/abs/2601.15195", "https://knip.dev/"], "canonical_file": "CardBench_zero_shot_cardinality_training/generate_queries_library/query_generator.py", "correlation_key": "fp|990476a2712aa5e541e4a411ad6371df0e5422df0cb3f0839afbc483e01eb748"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "CardBench_zero_shot_cardinality_training/generate_queries_library/query_generator_v2.py"}, "region": {"startLine": 1}}}]}, {"ruleId": "CFG006", "level": "warning", "message": {"text": "[CFG006] Missing .gitignore: No .gitignore file. Risk of committing secrets and build artifacts."}, "properties": {"repobilityId": 23095, "scanner": "repobility-threat-engine", "fingerprint": "c65fc71ce58c37a0e07837c0fe294108b731c43ef16027a2f0971c757bbe9a16", "category": "practices", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "No .gitignore file found in repository root", "evidence": {"reason": "No .gitignore file found in repository root", "rule_id": "CFG006", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "repo|practices|cfg006"}}}, {"ruleId": "SEC017", "level": "warning", "message": {"text": "[SEC017] Unbounded Input to LLM/External API: User input is passed to an LLM or external AI API (OpenAI, Anthropic, etc.) without any visible length or size validation. This creates two risks: (1) Cost abuse \u2014 an attacker can send extremely long inputs to burn through your API credits (a single 128K-token request to GPT-4 costs ~$4, and automated attacks can drain budgets in minutes). (2) Context stuffing \u2014 oversized inputs can push your system prompt out of the context window, effectively disab"}, "properties": {"repobilityId": 23092, "scanner": "repobility-threat-engine", "fingerprint": "01fbb685ae8def369ab44edcdcb0fd4f7ba48bf48eb38c203df65b909b9a82a8", "category": "llm_injection", "severity": "medium", "confidence": 0.8, "triageState": "open", "verdict": "likely", "isResolved": false, "reason": "This file sends user input to an LLM with no visible length check or rate limit. Risks: (1) cost abuse \u2014 automated long inputs drain API budget ($4/request at 128K tokens on GPT-4), (2) context stuffing \u2014 oversized input pushes system prompt out of context window, disabling safety rules. Add input length validation before the API call.", "evidence": {"reason": "This file sends user input to an LLM with no visible length check or rate limit. Risks: (1) cost abuse \u2014 automated long inputs drain API budget ($4/request at 128K tokens on GPT-4), (2) context stuffing \u2014 oversized input pushes system prompt out of context window, disabling safety rules. Add input length validation before the API call.", "rule_id": "SEC017", "scanner": "repobility-threat-engine", "confidence": 0.8, "correlation_key": "fp|01fbb685ae8def369ab44edcdcb0fd4f7ba48bf48eb38c203df65b909b9a82a8"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "EgoSocial/Phi4/phi4_video_audio_SI_baseline_audio2text_conv_all_graph.py"}, "region": {"startLine": 297}}}]}, {"ruleId": "SEC017", "level": "warning", "message": {"text": "[SEC017] Unbounded Input to LLM/External API: User input is passed to an LLM or external AI API (OpenAI, Anthropic, etc.) without any visible length or size validation. This creates two risks: (1) Cost abuse \u2014 an attacker can send extremely long inputs to burn through your API credits (a single 128K-token request to GPT-4 costs ~$4, and automated attacks can drain budgets in minutes). (2) Context stuffing \u2014 oversized inputs can push your system prompt out of the context window, effectively disab"}, "properties": {"repobilityId": 23091, "scanner": "repobility-threat-engine", "fingerprint": "3755c39c874d7a4e084d280ed1073ee0e1b45632c35d69fe876010a8eb5a9b0e", "category": "llm_injection", "severity": "medium", "confidence": 0.8, "triageState": "open", "verdict": "likely", "isResolved": false, "reason": "This file sends user input to an LLM with no visible length check or rate limit. Risks: (1) cost abuse \u2014 automated long inputs drain API budget ($4/request at 128K tokens on GPT-4), (2) context stuffing \u2014 oversized input pushes system prompt out of context window, disabling safety rules. Add input length validation before the API call.", "evidence": {"reason": "This file sends user input to an LLM with no visible length check or rate limit. Risks: (1) cost abuse \u2014 automated long inputs drain API budget ($4/request at 128K tokens on GPT-4), (2) context stuffing \u2014 oversized input pushes system prompt out of context window, disabling safety rules. Add input length validation before the API call.", "rule_id": "SEC017", "scanner": "repobility-threat-engine", "confidence": 0.8, "correlation_key": "fp|3755c39c874d7a4e084d280ed1073ee0e1b45632c35d69fe876010a8eb5a9b0e"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "EgoSocial/Phi4/phi4_video_audio_SI_baseline.py"}, "region": {"startLine": 117}}}]}, {"ruleId": "SEC017", "level": "warning", "message": {"text": "[SEC017] Unbounded Input to LLM/External API: User input is passed to an LLM or external AI API (OpenAI, Anthropic, etc.) without any visible length or size validation. This creates two risks: (1) Cost abuse \u2014 an attacker can send extremely long inputs to burn through your API credits (a single 128K-token request to GPT-4 costs ~$4, and automated attacks can drain budgets in minutes). (2) Context stuffing \u2014 oversized inputs can push your system prompt out of the context window, effectively disab"}, "properties": {"repobilityId": 23090, "scanner": "repobility-threat-engine", "fingerprint": "83900083ec4a2ca619bf3e63e70a491a77d66786fc184023cf2ce78e2f9b17a2", "category": "llm_injection", "severity": "medium", "confidence": 0.8, "triageState": "open", "verdict": "likely", "isResolved": false, "reason": "This file sends user input to an LLM with no visible length check or rate limit. Risks: (1) cost abuse \u2014 automated long inputs drain API budget ($4/request at 128K tokens on GPT-4), (2) context stuffing \u2014 oversized input pushes system prompt out of context window, disabling safety rules. Add input length validation before the API call.", "evidence": {"reason": "This file sends user input to an LLM with no visible length check or rate limit. Risks: (1) cost abuse \u2014 automated long inputs drain API budget ($4/request at 128K tokens on GPT-4), (2) context stuffing \u2014 oversized input pushes system prompt out of context window, disabling safety rules. Add input length validation before the API call.", "rule_id": "SEC017", "scanner": "repobility-threat-engine", "confidence": 0.8, "correlation_key": "fp|83900083ec4a2ca619bf3e63e70a491a77d66786fc184023cf2ce78e2f9b17a2"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "EgoSocial/Phi4/phi4_video_audio_SI_baseline_audio2text_conv_all.py"}, "region": {"startLine": 215}}}]}, {"ruleId": "SEC034", "level": "warning", "message": {"text": "[SEC034] Log Injection / Log Forging \u2014 unsanitized user input in log: User input is logged without sanitizing newlines or control characters. Attackers inject `\\n` to forge fake log entries, hide tracks, or exploit downstream log parsers (SIEM, splunk). Combined with template injection this can escalate to RCE (CVE-2021-44228 log4shell). CWE-117."}, "properties": {"repobilityId": 23085, "scanner": "repobility-threat-engine", "fingerprint": "1936ce3de6eb0f8c0a11d611b0b0385d80ab89a4d6f8f0d9115304eb8f2b46da", "category": "log_injection", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": "logger.info(f\"image logging not implemented for {args", "reason": "Pattern matched with no mitigating context found", "rule_id": "SEC034", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "fp|1936ce3de6eb0f8c0a11d611b0b0385d80ab89a4d6f8f0d9115304eb8f2b46da"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "CoDi/training_scripts/train_codi_flax.py"}, "region": {"startLine": 186}}}]}, {"ruleId": "SEC015", "level": "warning", "message": {"text": "[SEC015] Insecure Randomness for Security: Weak PRNG used in security-sensitive context. Output is predictable."}, "properties": {"repobilityId": 23081, "scanner": "repobility-threat-engine", "fingerprint": "dcd52fe6641473abf7a9d03c6341ed19cc91d678fd6c911d5fcf1169f61bc9ea", "category": "crypto", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Security-sensitive keyword found nearby \u2014 weak PRNG is risky here", "evidence": {"match": "random.choice(key", "reason": "Security-sensitive keyword found nearby \u2014 weak PRNG is risky here", "rule_id": "SEC015", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "code|crypto|token|145|sec015"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "abstract_nas/synthesis/primer_sequential.py"}, "region": {"startLine": 145}}}]}, {"ruleId": "SEC007", "level": "warning", "message": {"text": "[SEC007] Unsafe Deserialization: Unsafe deserialization can execute arbitrary code."}, "properties": {"repobilityId": 23079, "scanner": "repobility-threat-engine", "fingerprint": "83311ac0a6163709c7e24ecb4820e691236c8d2c03af50d53d4af69ca271cd64", "category": "deserialization", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": "pickle.load(", "reason": "Pattern matched with no mitigating context found", "rule_id": "SEC007", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "code|deserialization|token|201|sec007"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "caltrain/glm_modeling/__init__.py"}, "region": {"startLine": 201}}}]}, {"ruleId": "SEC007", "level": "warning", "message": {"text": "[SEC007] Unsafe Deserialization: Unsafe deserialization can execute arbitrary code."}, "properties": {"repobilityId": 23078, "scanner": "repobility-threat-engine", "fingerprint": "40d59c543694c7379c75bfa2d7dccc6b772e5834c6b47b88b4bd87f6b48fb40f", "category": "deserialization", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": "pickle.loads(", "reason": "Pattern matched with no mitigating context found", "rule_id": "SEC007", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "code|deserialization|token|71|sec007"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "abps/py_hashed_replay_buffer.py"}, "region": {"startLine": 71}}}]}, {"ruleId": "SEC007", "level": "warning", "message": {"text": "[SEC007] Unsafe Deserialization: Unsafe deserialization can execute arbitrary code."}, "properties": {"repobilityId": 23077, "scanner": "repobility-threat-engine", "fingerprint": "ed07a67c620cb020eff6f40e12410441c2b2636273b55d468236ab136d0445e7", "category": "deserialization", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": "pickle.loads(", "reason": "Pattern matched with no mitigating context found", "rule_id": "SEC007", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "code|deserialization|abstract_nas/utils.py|118|sec007"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "abstract_nas/utils.py"}, "region": {"startLine": 118}}}]}, {"ruleId": "SEC011", "level": "warning", "message": {"text": "[SEC011] Unsafe PyTorch Model Loading: torch.load() uses pickle internally and can execute arbitrary code from untrusted model files."}, "properties": {"repobilityId": 23075, "scanner": "repobility-threat-engine", "fingerprint": "f8f9b32bb5dcdbac9ef4b262e64b3a602d714ba653141d571fbd36efa0de11a7", "category": "deserialization", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": "torch.load(", "reason": "Pattern matched with no mitigating context found", "rule_id": "SEC011", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "code|deserialization|costar/utils/ckpt.py|66|sec011"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "COSTAR/utils/ckpt.py"}, "region": {"startLine": 66}}}]}, {"ruleId": "SEC011", "level": "warning", "message": {"text": "[SEC011] Unsafe PyTorch Model Loading: torch.load() uses pickle internally and can execute arbitrary code from untrusted model files."}, "properties": {"repobilityId": 23074, "scanner": "repobility-threat-engine", "fingerprint": "2fc22bd1ac3aa3012d1965776b2be043c6d4b41b40779cad5dd2e344a3c330b0", "category": "deserialization", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": "torch.load(", "reason": "Pattern matched with no mitigating context found", "rule_id": "SEC011", "scanner": "repobility-threat-engine", "confidence": 1.0, "correlation_key": "code|deserialization|knf/evaluation.py|46|sec011"}}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "KNF/evaluation.py"}, "region": {"startLine": 46}}}]}, {"ruleId": "SEC011", "level": "warning", "message": {"text": "[SEC011] Unsafe PyTorch Model Loading: torch.load() uses pickle internally and can execute arbitrary code from untrusted model files."}, "properties": {"repobilityId": 23073, "scanner": "repobility-threat-engine", "fingerprint": "3a09e8344d41948af5d388946303825f6a42a5decadd2730c6f198f805bada4d", "category": "deserialization", "severity": "medium", "confidence": 1.0, "triageState": "open", "verdict": "confirmed", "isResolved": false, "reason": "Pattern matched with no mitigating context found", "evidence": {"match": 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