IBM/ares
AI Robustness Evaluation System
What's novel
AI Robustness Evaluation System
Code Analysis
10 files read · 3 roundsARES is a plugin-based LLM red-teaming orchestration framework that automates the pipeline of defining attack goals, executing attack strategies (direct requests, multi-turn, plugin-based) against target LLM connectors (HuggingFace, WatsonX, REST), and evaluating results via keyword matching, LLM ju
Strengths
Clean plugin architecture with well-defined ABCs (Connector, AttackStrategy, AttackGoal, AttackEval) and Pydantic-based config validation enables extensibility across 20+ attack plugins. The orchestration layer (RedTeamer) cleanly separates concerns between goal loading, strategy execution, target interaction, and evaluation, with good support for guardrails, multi-turn sessions, and multiple output formats (JSON, CSV, HTML, dashboard).
Weaknesses
The plugin loading mechanism in builder.py uses fragile string-containment matching via inspect.getmembers rather than direct class lookup, which could silently load the wrong class. The base AttackStrategy.attack() method has a subtle bug where it checks self.attack_results before self.attack_goals, and the multi-turn strategy and LLM evaluator implementations (which contain the most novel logic) could not be fully verified due to budget constraints.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
852
Contributors
15
Files
477
Active weeks
30
Repository
Language
Python
Stars
37
Forks
20
License
Apache-2.0