Submission: AgentBusters - configforge#18
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Pull request overview
This PR adds "ConfigForge", a security arena scenario that tests whether AI assistants can be tricked into generating dangerous configuration files (LangChain serialization, YAML injection, Pickle patterns) that would execute malicious code when deserialized by downstream systems. The submission is inspired by OWASP LLM06 (Insecure Output Handling) and generalizes the K8s Kill scenario to multiple configuration systems.
Changes:
- Adds comprehensive ConfigForge scenario plugin with behavioral simulation engine
- Includes test results showing attack success in Round 2
- Provides extensive documentation (README, ARCHITECTURE.md, architecture diagrams, example data)
Reviewed changes
Copilot reviewed 17 out of 17 changed files in this pull request and generated 5 comments.
Show a summary per file
| File | Description |
|---|---|
| plugin.py | Main ConfigForgePlugin implementing ScenarioPlugin interface with evaluation logic |
| simulator.py | Runtime simulation engine modeling LangChain/YAML/Pickle deserializers |
| llm_judge.py | LLM-based semantic analysis via vLLM/OpenRouter |
| extractor.py | Multi-format config extraction (JSON, YAML, code blocks) |
| models.py | Data models (Severity, OperationType, Operation, SimulationResult, etc.) |
| scenario_configforge.toml | Scenario configuration with model and timeout settings |
| run_with_timeout.py | Wrapper script with httpx timeout patching for slow vLLM inference |
| test_results/*.json | Test results showing baseline passed and attack succeeded in Round 2 |
| ARCHITECTURE.md | Comprehensive design documentation (1307 lines) |
| README.md | User-facing setup and usage instructions |
| architecture.drawio | Architecture diagram in draw.io format |
| data/*.json | Example chains and dangerous patterns reference data |
| .env.example | Environment variable template |
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scenarios/security_arena/submissions/AgentBusters/configforge/llm_judge.py
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Demo Video
https://youtu.be/D991loZgFS4
Insecure Output Handling - Testing if AI assistants generate dangerous configuration files (e.g., LangChain/YAML injection) that execute malicious code when deserialized by downstream systems