Llm Authenticity Detection
D 60 completed
Other
library / python · tiny
37
Files
4,469
LOC
0
Frameworks
7
Languages
Pipeline State
completedRun ID
#1534223Phase
doneProgress
0%Started
2026-04-16 14:55:28Finished
2026-04-16 14:55:28LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
25.63Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47371
Member of a group with 311 similar repo(s) — canonical #1523155 view group →
About: code-quality intelligence by Repobility · https://repobility.com
🧪 Code Distillation
Browse all specs →AI Prompt
Create a Python library for model fingerprinting to detect if an LLM API is being proxied or disguised. I need it to support testing against both official OpenAI APIs and custom third-party endpoints. The system should analyze four detection layers: API/protocol, cognitive/prompt, alignment/review, and logic/math. It should read configuration from `config.yaml` and generate a detailed report showing the detection status for each layer, including a final conclusion and a pass rate.
python library llm ai security fingerprinting api detection
Generated by gemma4:latest
Catalog Information
Create a Python library for model fingerprinting to detect if an LLM API is being proxied or disguised. I need it to support testing against both official OpenAI APIs and custom third-party endpoints. The system should analyze four detection layers: API/protocol, cognitive/prompt, alignment/review, and logic/math. It should read configuration from config.yaml and generate a detailed report showing the detection status for each layer, including a final conclusion and a pass rate.
Tags
python library llm ai security fingerprinting api detection
Quality Score
D
59.5/100
Structure
55
Code Quality
74
Documentation
54
Testing
20
Practices
60
Security
100
Dependencies
90
Strengths
- Consistent naming conventions (snake_case)
- Good security practices — no major issues detected
Weaknesses
- No LICENSE file — legal ambiguity for contributors
- No CI/CD configuration — manual testing and deployment
- 3 bare except/catch blocks swallowing errors
- 225 duplicate lines detected — consider DRY refactoring
Recommendations
- Add a test suite — start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
- Replace bare except/catch blocks with specific exception types
Languages
Frameworks
None detected
Symbols
method73
variable53
constant35
class24
function5
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BinComp Dependency Hardening
All packages →3 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.