Hackagent

HackAgent fills a critical gap in AI security. As agents become more autonomous, traditional security tools don't cover prompt injection, jailbreaking, or goal hijacking. HackAgent automates testing for these with a modular engine that combines attack techniques like AutoDAN-Turbo, PAIR, and TAP. We'd reach for this when auditing an agent before production deployment. The pre-built datasets (AgentHarm, JailbreakBench, HarmBench) save setup time, and the terminal UI gives real-time feedback. It works out of the box with Google ADK, OpenAI SDK, LangChain, Ollama, vLLM, and LiteLLM — covering the major frameworks. Where it bites: it's CLI-only and Python-based, so non-technical users will struggle. There's no GUI, and customizing attacks requires scripting. For traditional web or API penetration testing, you'd want other tools — HackAgent is AI-agent specific. Compared to alternatives like Garak (LLM vulnerability scanner), HackAgent is more agent-focused and supports more attack techniques, while Garak covers a broader range of LLM risks. For red-teaming agents specifically, HackAgent is the stronger choice. In practice, the responsible use guidelines are clear — only test with permission. The tool itself is well-documented and the architecture is straightforward: inputs (goals/datasets) → attack engine → target agent → outputs (reports). You'll need to bring your own LLM API keys for the generator and judge models. Bottom line: for security researchers and developers building agentic systems, HackAgent is a must-have in the pre-deployment testing toolkit. It's free, open-source, and backed by research.