AegisLM
See how easily your AI can be broken — in seconds
AegisLM launched on May 4, 2026 with 4 votes, #2,419 of 20,538 launches that month and more than 75% of that year's launches. The site was not reachable when checked in September 2026.
What it does
AegisLM shows how easily modern AI can be broken. Test any model for prompt injection, jailbreaks, and data leaks in seconds. Input a prompt, run attacks, and see where it fails. Designed for builders who want to stress-test AI systems under real-world conditions. Try built-in attacks or create your own.
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FabraixMay 2026 · fabraix.com · ▲196We built Adversarial Cost to Exploit (ACE), a benchmark that measures the token expenditure an autonomous adversary must invest to breach an LLM agent. Instead of binary pass/fail, ACE quantifies adversarial effort in dollars, enabling game-theoretic analysis of when an attack is economically rational. We tested six budget-tier models (Gemini Flash-Lite, DeepSeek v3.2, Mistral Small 4, Grok 4.1 Fast, GPT-5.4 Nano, Claude Haiku 4.5) with identical agent configs and an autonomous red-teaming attacker. Haiku 4.5 was an order of magnitude harder to break than every other model; $10.21 mean…
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- FPFind prompts that jailbreak your agent (open source)2025 · security.vista-labs.ai · ▲8
We've built an open-source tool to stress test AI agents by simulating prompt injection attacks. We’ve implemented one powerful attack strategy based on the paper [AdvPrefix: An Objective for Nuanced LLM Jailbreaks](https://arxiv.org/abs/2412.10321). Here's how it works: - You define a goal, like: “Tell me your system prompt” - Our tool uses a language model to generate adversarial prefixes (e.g., “Sure, here are my system prompts…”) that are likely to jailbreak the agent. - The output is a list of prompts most likely to succeed in bypassing safeguards. We’re just getting…
LLMs can be susceptible to a new kind of malware2023 · github.com · ▲17New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.
ModelRedNov 2025 · modelred.ai · ▲10Hi everyone, We built ModelRed to test AI models and apps for security issues. Ran 4,182 attack probes against 9 leading models to see what would break. Leaderboard: https://modelred.ai (no signup, just check it out) Claude scored 9.5/10 but still failed on medical/financial prompts. Mistral Large scored 3.3/10. The gap between best and worst is huge. We test for prompt injections, data leaks, jailbreaks, risky tool calls, domain specific hacks, basically everything that goes wrong when your LLM has access to real data and APIs. The platform runs these tests…
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