hackagent is an open-source red-team toolkit that throws research-backed jailbreak, prompt injection, and tool misuse attacks at your own AI models and agents, then scores every reply with judge models.
| Tool | AISecurityLab/hackagent — open-source red-team toolkit for AI models and agents, with judge-scored attack campaigns |
| Primary Use | Running repeatable adversarial campaigns (jailbreaks, prompt injection, tool misuse) against AI agents you own, via hackagent TUI, CLI YAML campaigns, and hackagent web dashboard |
| Primary Use | Measuring per-attack success rates against your own models and agents in authorized assessments and AI safety evaluation pipelines |
| Safe Use | Strictly authorized security testing: the README states you must test systems you own or have written permission to test, and report findings via coordinated disclosure |
| Telemetry Note | By default results stay in a local database with no API key required, so activity is contained to the operator's machine; defenders observing LLM-guarding deployments should watch judge-model traffic and campaign YAML artifacts in CI |
hackagent from AISecurityLab positions itself as "the open-source red-team toolkit for AI models and agents," and the framing matters: this is not a scanner that probes third-party endpoints, but a harness for attacking AI systems you control with research-backed techniques — jailbreaks, prompt injection, and tool misuse — and then having judge models score every reply. The output that differentiates it from a pile of PoC scripts is a measured success rate per attack, which turns fuzzy questions like "is my agent jailbreakable" into a number you can track before and after a mitigation. That measurement loop is the actual product here, not the individual attacks.
The repository shows healthy engineering signals for a young security tool: 516 stars, Apache-2.0 licensing, Python 3.10+ as the stated runtime, a ci.yml GitHub Actions workflow badge, and Codecov coverage tracking. Topics tag it as ai-agents, jailbreak, pentesting, and red-team, which matches the README's self-description. The project ships as a PyPI package (hackagent), with the extra [tui] pulling in the terminal UI dependencies, and the docs mention standalone binaries that need no Python at all — a sensible accommodation for evaluators who don't want to manage interpreter environments.
Installation is a one-liner, and it's one of the two examples worth showing: pip install 'hackagent[tui]' followed by running hackagent bare, which opens the terminal UI. From there the intended workflow is to start from an example, modify what needs modifying, and run. The second canonical flow is fully local: ollama pull gemma3:4b, then hackagent examples copy h4rm3l-demo, then hackagent campaign run h4rm3l-demo.yaml. The significance of that local path is hard to overstate for authorized testing — you can evaluate a small model like gemma3:4b on your own hardware, with no API key, before ever pointing the tool at a production-grade target.
The campaign-as-YAML design is the architectural detail a senior operator will appreciate most. The same campaign is described as one YAML file you can edit, validate, and run, from a shell or in CI. That makes adversarial evaluation composable: a campaign definition can live in version control, get code-reviewed like any other test asset, and execute as a regression gate whenever an agent's system prompt, tool schema, or model version changes. The hackagent examples copy flow suggests campaigns are copyable templates rather than hand-blank files, lowering the barrier for teams new to LLM red teaming.
Three interfaces cover the same engine. The terminal UI (with a demonstrated GIF of building and running a campaign), the CLI for scripted and CI use, and a dashboard via hackagent web for browsing runs, results, and traces. There's also a hosted variant at app.hackagent.dev for account-based browsing. The trace browsing deserves note: for judge-scored adversarial testing, the ability to inspect why a judge marked a reply as a successful jailbreak or a failed one is what makes results auditable rather than a black-box score. The project also exposes an API with a Redoc schema at api.hackagent.dev, indicating the tool is designed for integration, not just interactive poking.
Internally, the README describes a clean separation between attacks, judges, and runs, documented under its Concepts pages. Attacks generate adversarial prompts; judges score replies; a run executes the pairing and persists outcomes. The local-first storage posture — "results go to a local database unless a campaign says otherwise" — means the default data flow keeps potentially sensitive evaluation traffic (including jailbreak payloads and model responses) on the operator's machine. That's the right default for a security tool, though operators should be aware that switching to hosted or cloud-model campaigns changes that posture, and campaign YAML should be reviewed accordingly.
The attack taxonomy the README names — jailbreaks, prompt injection, tool misuse — maps directly onto the failure modes practitioners see in agent deployments. Jailbreaks target the model's alignment boundary; prompt injection targets the trust boundary between untrusted context (retrieved documents, user input, tool output) and the agent's instructions; tool misuse probes whether an agent with capabilities like code execution or API calls can be manipulated into using them against policy. The h4rm3l-demo example name hints at integration with the HarmBench-adjacent research lineage, consistent with the "research-backed techniques" claim, but the README itself doesn't enumerate specific attack implementations — the reference docs at docs.hackagent.dev hold that detail, so treat the attack library as documented externally rather than in the repo front page.
The responsible-use section is explicit and worth quoting in substance: hackagent is for authorized security testing only; test systems you own or have written permission to test; report findings through coordinated disclosure, with a dedicated responsible-disclosure page and SECURITY.md. This is a legitimate AI safety and defensive-research tool: measuring how exposed an agent is, and verifying whether a fix helped, is precisely the workflow ML security teams and AI vendors need before shipping agents with tool access. The license block reiterates that the tool is intended for security research and AI safety improvement.
Where does it fit in an authorized workflow? Think of it as the adversarial counterpart to functional testing for agent development: define a campaign covering the jailbreak and injection classes relevant to your deployment, run it against staging, inspect judge-scored traces in the dashboard, fix (prompt hardening, input sanitization, tool permissioning), and re-run to confirm the success rate dropped. The CI-friendly YAML format means this can be a recurring gate rather than a one-off audit. For purple teams, the per-attack success rates also give defenders a concrete signal for which mitigations actually move the needle versus which are security theater.
From a defensive telemetry standpoint, hackagent running locally is largely invisible to outside observers — that's the point of a local database and local models. What defenders of AI-facing infrastructure should care about is the attack classes it mechanizes: if your agent ingests untrusted text, prompt injection campaigns like the ones this tool automates are a realistic threat model regardless of who runs the tool. Instrumenting your agent to log anomalous tool calls, refusal failures, and instruction-override patterns gives you the observability to detect when something — hackagent in authorized testing, or a real adversary — is probing those boundaries. The project's own discussion forum and the ais@ai4i.it contact channel suggest an active maintainer community worth engaging for teams adopting it.
Caveats before adoption: the README is marketing-forward and thin on the actual attack catalog, judge methodology, and scoring calibration, so a serious evaluation requires reading the docs site and the campaign file reference. Judge models are themselves fallible — success rates are only as trustworthy as the judge — and the demo defaulting to a 4-billion-parameter local model is great for onboarding but not representative of frontier-agent attack surface. Still, as an Apache-2.0, CI-integrable, measurement-first red-team harness for AI agents, hackagent fills a genuine gap between one-off jailbreak PoCs and enterprise AI security platforms.
AISecurityLab/hackagent.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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