2025
XTRAP
Autonomous Red Team System
Red TeamingAdversarial AIMulti-AgentSafety Evaluation
Overview
An autonomous adversarial evaluation system that generates, refines, and memorizes successful attack patterns against language models. Uses multi-model attacker/defender loops with recursive refinement to continuously improve attack effectiveness.
Challenges
- Manual red teaming doesn't scale across model updates
- Attack patterns become stale as models are patched
- Need systematic coverage of vulnerability categories
- Evaluation requires consistent scoring methodology
Approach
- Built attacker agent that generates novel jailbreak attempts
- Implemented defender evaluation with constitutional scoring
- Created attack memory bank with embedding-based retrieval
- Designed recursive refinement loop for failed attempts
Multi-Model Attack Loop
python
class AdversarialLoop:
def __init__(self, attacker_model, defender_model):
self.attacker = attacker_model
self.defender = defender_model
self.memory = AttackMemoryBank()
async def run_attack_cycle(self, target_behavior: str):
# Retrieve similar successful attacks
similar_attacks = self.memory.retrieve(target_behavior, k=5)
# Generate new attack variants
attack = await self.attacker.generate(
target=target_behavior,
examples=similar_attacks,
strategy="recursive_refinement"
)
# Evaluate against defender
response = await self.defender.respond(attack.prompt)
score = self.evaluate_success(response, target_behavior)
if score > 0.8:
self.memory.store(attack, score)
return {"success": True, "attack": attack}
# Recursive refinement on failure
return await self.refine_attack(attack, response)Tech Stack
PythonOpenAIAnthropicLangGraphVector DBStructured Outputs
Outcomes
- Discovered 58+ verified jailbreaks in Gray Swan Challenge
- Achieved top ~1% ranking in adversarial evaluation
- Patterns contributed to model safety improvements