Leaderboard Comparing LLM Agent Security on System Prompt Leakage and Attack Probes
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Updated
May 2, 2026 - Python
Leaderboard Comparing LLM Agent Security on System Prompt Leakage and Attack Probes
Automated red-teaming pipeline benchmarking LLM safety across 150 adversarial prompts. Achieves 81.1% ASR across 3 models with zero human annotation.
A governed range for testing AI agent defenses: Agent Theater’s earned-trust tiers and human oversight, plus an adversarial arena that attacks each OWASP Top 10 for Agentic Applications risk. Measured results, honest gaps, and what it would take to close them.
Modular LLM security research framework for benchmarking prompt jailbreak attacks, defense strategies, and LLM-as-Judge evaluation using Python, Ollama, FastAPI, and Mistral 7B.
Sandboxed red-teaming harness for LLM agents: an attacker agent tries prompt injection, indirect injection and jailbreaks on a defender agent, and a judge scores attack success rate, policy violations, trajectory errors and cost.
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