mirror of
https://github.com/msoedov/agentic_security.git
synced 2026-06-24 06:09:55 +02:00
Alexander Myasoedov
32f103acbc
Add LLM-based refusal classifier inspired by Promptmap's dual-LLM architecture. The controller LLM evaluates whether an attack succeeded by analyzing the target's response against pass/fail conditions. - Create LLMRefusalClassifier plugin integrating with existing system - Support OpenAI and Anthropic providers with lazy initialization - Add configurable system prompts and pass/fail conditions - Include 20 unit tests for comprehensive coverage
24 lines
1.1 KiB
Plaintext
24 lines
1.1 KiB
Plaintext
## Codebase Patterns
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- Use async-first patterns for all I/O operations (httpx, asyncio)
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- Plugin architecture with abstract base classes for extensibility
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- Use table-driven tests with inline_snapshot where possible
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- Tests are organized in tests/unit/, tests/integration/, tests/system/
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- Use absolute imports, avoid relative imports except within same module
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- Minimize docstrings, use quick returns, avoid abstractions without proven need
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---
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## 2026-01-28 - Initial Setup
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- Created PRD with 5 user stories based on research from promptfoo, promptmap, and FuzzyAI
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- Key improvements identified:
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1. Dual-LLM evaluation (from Promptmap)
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2. YAML rule system (from Promptmap)
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3. Composable fuzzing chains (from FuzzyAI)
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4. Unified provider abstraction (from FuzzyAI)
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5. Hybrid refusal detection (combining approaches)
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- **Learnings for future iterations:**
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- Existing refusal detection is in agentic_security/probe_actor/refusal.py with RefusalClassifierPlugin system
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- Attack data modules are in agentic_security/probe_data/modules/
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- Security utilities are in agentic_security/core/security.py
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---
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