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Genbounty-LLM-Hunter-Community-Free
by airtasystems
Community toolkit for adversarial LLM and agent red-teaming
Python
Updated Jul 31, 2026
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Summary
Performs adversarial testing and red-team workflows against LLMs, agents, and MLops pipelines. Tool Use Pattern
The Value Proposition
As agents and LLMs are delegated more autonomy, proactive adversarial testing is essential to surface real-world failure modes and trust signals. This toolkit makes red-team practices accessible and repeatable, so teams can quantify weaknesses before deployment. Event-Driven Agent Pattern to surface real-world failure modes and trust signals. It packages community attack patterns into reproducible tests, enabling Mutual Verification Pattern style checks.
When to Use
Security engineers, MLops teams, and internal red teams running pre-production adversarial tests on LLMs and agent pipelines. Agent Protocol
Real-World Examples
- Run repeatable prompt-injection and jailbreak tests against models before deployment
- Automate adversarial scenarios to validate agent delegation and failure modes
- Build an internal bug-bounty style workflow for LLM vulnerabilities
- Catalog community attack patterns to improve model hardening and monitoring
Topics
adversarial-testingagent-testingai-bug-bountyai-red-teamai-red-teamingai-safetyai-securityllm-securityllm-testing
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Keywords
ai-red-teamingadversarial-testingagent-to-agent evaluationmulti-agent trust