Hackagent
Free, open-source Python toolkit that red-teams AI agents against prompt injection, jailbreaking, goal hijacking, and tool misuse before attackers find the
HackAgent is the right pick if you write Python, have written permission to test your own agents, and want broad attack coverage plus real benchmark datasets without a license fee. Eleven techniques, six framework adapters, and ten benchmark presets in one pip install is a wider starting kit than you typically assemble by hand across Garak or PyRIT. The July 2026 dependency scanner also gives you a second job for the same binary. The trade-off is structural, not fixable: it is a CLI/SDK for pre-deployment testing only, with no web GUI and no runtime guarding, so pair it with a monitoring layer rather than treating it as one.
Verified 1d ago · liveness 70/100 · cite: rightaichoice.com/tools/hackagent
- Security researchers auditing AI agents before deployment
- AI safety practitioners running authorized red-team exercises
- Python developers building agents on LangChain, LiteLLM, or the OpenAI SDK
- ML engineers benchmarking model robustness against adversarial prompts
- Non-technical users who need a GUI instead of a Python CLI
- Anyone testing a system without explicit permission from its owner
- Teams needing runtime monitoring or live guardrails in production
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Skip HackAgent if you need a point-and-click web interface or runtime guardrails for a live production agent — it is a Python CLI/SDK built for pre-deployment, authorized red-teaming by people who are comfortable reading terminal output.
The package itself is free, but the attack engine calls an LLM as generator and another as judge, so every run against a benchmark set spends real API tokens that you pay for separately.
The seed data records HackAgent as free and open source, which puts it at the inexpensive end of agent security testing against commercial red-team platforms and vendor-managed AI security suites. The real cost is the generator and judge LLM tokens you supply at runtime plus the engineering time to wire up an adapter — so it fits well-funded security teams and solo researchers alike, but the total spend scales with how hard you attack.
In short
Hackagent — Free, open-source Python toolkit that red-teams AI agents against prompt injection, jailbreaking, goal hijacking, and tool misuse before attackers find the. Best for Security researchers auditing AI agents before deployment, AI safety practitioners running authorized red-team exercises, Python developers building agents on LangChain, LiteLLM, or the OpenAI SDK. Free to use.
What's new in Hackagent
Checked yesterdayAcross the latest 1 update: 1 feature update.
What people actually say about Hackagent — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
22 mentions across 3 sources (Hacker News, YouTube, GitHub) · researched Aug 20, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Free and open-source with no cost barriers
- +Eleven attack techniques including AdvPrefix, PAIR, and TAP
- +Supports multiple agent frameworks: ADK, OpenAI, LangChain, etc.
- +Integrates pre-built benchmarks like AgentHarm and JailbreakBench
- +Interactive TUI for real-time attack visualization
- −Early-stage with few stars and limited community
- −Steep learning curve for configuring multi-LLM roles
- −Minimal direct user reviews or case studies
- −Documentation may be sparse for complex setups
- −Potential instability due to ongoing changes
- • No direct costs, but users may incur LLM API costs when using generators and judges
- • Time investment for setup and configuration
Viability Score
How well maintained and how widely used is Hackagent? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Automated prompt injection testing against AI agents
- Jailbreak attack techniques including AdvPrefix, AutoDAN-Turbo, PAIR, and TAP
- Goal hijacking and tool misuse evaluation for agentic systems
- 11 attack techniques including FlipAttack, BoN, h4rm3l, CipherChat, PAP, and Static Template
- Pre-built benchmark datasets: AgentHarm, JailbreakBench, HarmBench, AdvBench, StrongREJECT
- Additional benchmark presets: BeaverTails, SALAD-Bench, WMDP, AIR-Bench, ToxicChat
- Custom dataset import from HuggingFace, URL, or JSON/CSV/JSONL/TXT file
- Modular attack engine with separate generator, judge, and target LLM roles
- AutoDAN-Turbo summarizer component
- Category classifier for organizing attack outcomes
- Interactive terminal UI (TUI) with real-time attack progress and visualizations
- Report and dashboard output for attack results
- Python SDK with autogenerated API reference (hackagent v0.11.0)
- CLI dependency scanner for AI agent dependencies (added July 2026)
- Runs locally out of the box with optional cloud sync via HACKAGENT_API_KEY
About Hackagent
HackAgent is a Python SDK and CLI that automates red-teaming of AI agents. Instead of generic app-security scanning, it targets the failure modes specific to autonomous agents: prompt injection, jailbreaking, goal hijacking, and tool misuse. The attack engine splits the work across LLM roles — a generator LLM that crafts adversarial prompts, a judge LLM that scores whether the attack bypassed safety measures, and the target agent under test — with a separate category classifier for organizing outcomes. Eleven attack techniques ship in the box: AdvPrefix, AutoDAN-Turbo, PAIR, TAP, FlipAttack, BoN, h4rm3l, CipherChat, PAP, and Static Template. You can point it at pre-built benchmarks such as AgentHarm, JailbreakBench, HarmBench, AdvBench, StrongREJECT, BeaverTails, SALAD-Bench, WMDP, AIR-Bench, and ToxicChat, or bring your own dataset from HuggingFace, a local file in JSON, CSV, JSONL, or TXT format, or a URL. A terminal UI shows attack progress and result visualizations in real time, and the package runs locally out of the box with optional cloud sync via a HACKAGENT_API_KEY environment variable. Adapters exist for Google ADK, the OpenAI SDK, LiteLLM, LangChain, Ollama, and vLLM, so it slots into most agent builds. In July 2026 a CLI dependency scanner was added, letting you check agent dependencies for known vulnerabilities in the same tool. It is aimed at security researchers, AI safety practitioners, and Python developers doing authorized pre-deployment testing — not at non-technical users or anyone testing a system without the owner's explicit permission.
Behind the Verdict
HackAgent's strongest asset is that it is a bundle rather than a single technique. Where most agent red-team setups require you to assemble an attacker, a judge, and a scoring harness yourself, HackAgent ships all three as first-class components — a generator LLM that writes adversarial prompts, a judge LLM that decides whether the attack landed, and a category classifier that organizes the outcomes — then layers ten benchmark presets on top. That means you can go from pip install hackagent to results against AgentHarm or JailbreakBench in one session rather than one sprint. The technique list is the second differentiator. AdvPrefix, AutoDAN-Turbo, PAIR, TAP, FlipAttack, BoN, h4rm3l, CipherChat, PAP, and Static Template cover both automated optimization loops (PAIR, TAP, AutoDAN-Turbo) and cheaper template and cipher variants, which is what you want when you're sweeping an agent for cost-effective wins before spending budget on the expensive iterative attacks. The modular split is genuinely useful in practice: you can swap the judge model independently of the generator, and you can run AutoDAN-Turbo with its dedicated summarizer without touching the rest of the pipeline. Framework coverage is the third. Adapters for Google ADK, the OpenAI SDK, LiteLLM, LangChain, Ollama, and vLLM mean you are unlikely to be blocked by your agent's build. If you run local models through Ollama or vLLM, or you route everything through LiteLLM, you are a first-class target rather than an afterthought. Where it is honestly weak: this is a library for people who write Python and read CLI output. There is no web interface, and the results are aimed at a technical audience, so if your stakeholder report needs to land with non-engineers you will be doing a translation step. It is also strictly pre-deployment — it finds problems, it does not watch for them at runtime — so a monitoring or guardrail layer is a complement, not something you replace with this. The July 2026 dependency scanner is a small but real expansion: it moves HackAgent from pure attack simulation toward checking the third-party packages your agent pulls in. That's useful because a lot of agent risk arrives through dependencies rather than through the model's behaviour under prompt attack, and having both in one CLI reduces the number of tools you have to teach a new team member. On licensing and cost, the seed data records it as free and open source, which is consistent with the docs pointing at a public GitHub repo and an AI4I parent organization. Treat the cost of adoption as engineering time rather than a line item.
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Real-world workflow fit
Concrete scenarios for the personas Hackagent actually fits — and what changes day-one when you adopt it.
A team has built an internal support agent on LangChain and needs to test it before rollout. They pip install hackagent in a virtualenv, wire the LangChain adapter to the agent, and run the pre-built AgentHarm and JailbreakBench presets with PAIR and TAP as the attack techniques.
Outcome: The interactive TUI shows attack progress live, the judge scores which attacks bypassed the agent's guardrails, and they export a report of the failures to hand to the agent's developers.
They need repeatable, comparable numbers rather than a one-off probe. They run the same benchmark suite across two candidate agent frameworks — say Google ADK versus the OpenAI SDK — using identical generator and judge models so the only variable is the target.
Outcome: Category-classified results let them report which framework failed on which risk type, with a reproducible configuration their reviewers can re-run.
Before shipping, they run the July 2026 CLI dependency scanner over the agent's package set to catch known vulnerabilities in third-party dependencies, then follow up with FlipAttack and CipherChat runs to check whether cheap obfuscated prompts slip past the agent.
Outcome: They catch a vulnerable dependency and a set of low-effort jailbreak prompts in the same session, instead of finding either one after release.
Use Cases
- Red-teaming your own AI agent with prompt injection and jailbreak attacks before it ships
- Evaluating a custom agent's robustness against goal hijacking and tool misuse
- Running standardized benchmarks to compare security across agent frameworks
- Generating reproducible attack reports for internal audit or compliance review
- Scanning AI agent dependencies for known vulnerabilities via the July 2026 CLI scanner
- Sweeping an agent with cheap template and cipher attacks before spending on iterative techniques
- Swapping generator and judge models independently to isolate whether failures come from the target or the harness
Limitations
- HackAgent is for authorized security testing only — the docs state you must obtain explicit permission before testing any AI system.
- It is a Python SDK and CLI with no web interface, so it takes working knowledge of Python and the terminal; output is aimed at a technical reader and needs translating for a non-technical audience.
- It performs pre-deployment testing rather than runtime monitoring, so it will not guard a live agent in production.
- The engine depends on an LLM as both generator and judge, which means attack quality and scoring are partly a function of the models you wire in, and running iterative techniques like PAIR, TAP, or AutoDAN-Turbo against a benchmark set consumes model tokens and time.
- The docs also list explicit 'do nots': no testing without permission, no malicious exploitation, no terms-of-service violations, no irresponsible disclosure of exploits.
as of 2026-10-07
Verification history
We have re-verified Hackagent 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Hackagent tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open source
$0
Ideal for
Security researchers, AI safety practitioners, and Python developers who can run their own attack campaigns and supply their own generator and judge models.
What this tier adds
Starting tier — $0 for the toolkit itself; you cover the LLM token costs of generator, judge, and any summarizer model you wire in.
Where the pricing makes sense
The company stage and team size where Hackagent's pricing actually pencils out — and where peers do it cheaper.
The seed data records HackAgent as free and open source, which puts it at the inexpensive end of agent security testing against commercial red-team platforms and vendor-managed AI security suites. The real cost is the generator and judge LLM tokens you supply at runtime plus the engineering time to wire up an adapter — so it fits well-funded security teams and solo researchers alike, but the total spend scales with how hard you attack.
Setup time & first value
How long it actually takes to get something useful out of Hackagent — broken out by persona, not the marketing-page minute.
For a Python developer with an existing agent: expect a few minutes to create a virtualenv and pip install hackagent, then the bulk of the first session to wire up the right framework adapter (LangChain, LiteLLM, OpenAI SDK, Google ADK, Ollama, or vLLM) and choose generator and judge models. First results against a pre-built benchmark like JailbreakBench are reachable in a single sitting,
Switching to or from Hackagent
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Garak: map your existing probe set onto HackAgent's pre-built benchmark presets and re-run with PAIR or TAP for iterative attacks rather than static probes.
- →From PyRIT: reuse your orchestrated attack goals as HackAgent custom objectives and swap in the modular generator/judge split.
- →From hand-rolled red-team scripts: replace the custom attacker-and-scorer code with HackAgent's generator and judge components and keep your target adapter.
- →From a manual dataset in a spreadsheet: export it to CSV or JSONL and load it as a custom HackAgent dataset instead of copying prompts by hand.
- ↗To a runtime guardrail layer: keep HackAgent for pre-deployment testing and add monitoring for live traffic, since HackAgent does not watch running agents.
- ↗To a commercial AI security platform: move over if you need a web dashboard and pre-built reports for non-technical stakeholders rather than CLI output.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Hackagent”, and we withheld 6: 6 could not be judged, because “Hackagent” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Hackagent.
Official links
Tools that pair well with Hackagent
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Featured Head-to-Head Comparisons
Hackagent vs Audioeye
HackAgent and AudioEye serve fundamentally different purposes: security testing for AI agents vs. web accessibility compliance. Choose HackAgent if you need to red-team and audit agent-based AI systems with advanced attack techniques (free, open-source). Choose AudioEye if you require WCAG/ADA compliance for websites with automated scanning and expert support (paid, enterprise). They address distinct buyer needs and are not direct competitors.
Hackagent vs Sublime Security
For security researchers focused on AI agent red-teaming, Hackagent is a powerful free tool, especially with its recent dependency scanning addition. However, for organizations combating email threats, Sublime Security offers a modern, API-rich platform with low false positives. Choose based on your threat model: agent vs email security.
Hackagent vs Push Security
For organizations defending against browser-based attacks and securing AI tool usage in real-time, Push Security is the comprehensive choice with freemium pricing and deep integrations. Hackagent is the go-to open-source toolkit for red-teaming AI agents pre-deployment, but it lacks production monitoring. Evaluate based on whether your need is real-time defense (Push) or pre-deployment testing (Hackagent).
Alternatives to Hackagent
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Frequently Asked Questions
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