Hackagent
Free open-source AI agent security red-teaming toolkit
HackAgent is a strong free, open-source pick for red-teaming AI agents, packing a wide range of attack techniques and framework support. It demands Python/CLI skills and authorized use only. If you need a graphical dashboard or runtime monitoring, this isn't it—for pre-deployment testing, it's a solid choice. It compares favorably to Garak and PyRIT in breadth, making it a practical starting point for securing agentic AI.
Verified 3d ago · liveness 70/100 · cite: rightaichoice.com/tools/hackagent
- Security researchers auditing AI agents pre-deployment
- AI safety practitioners running authorized red-team tests
- Developers building agent-based applications who want to test defenses early
- ML engineers evaluating model robustness against adversarial attacks
- Non-technical users without Python/CLI experience
- Unauthorized testing — requires explicit permission from the system owner
- Traditional web/API security testing, since focus is AI-agent-specific threats
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Skip HackAgent if you are not comfortable with Python and the command line, or if you need a graphical dashboard or runtime monitoring rather than a pre-deployment testing toolkit.
While the core is free, using cloud sync may require an API key that could have associated costs depending on your usage.
HackAgent is free and open-source, making it a budget-friendly choice for independent researchers and small teams. Unlike commercial tools like Garak or PyRIT, there are no licensing fees. The main cost is the LLM API usage for running attacks, which is variable. For teams needing enterprise support or a managed service, commercial alternatives might be more suitable despite the cost.
In short
Hackagent — Free open-source AI agent security red-teaming toolkit. Best for Security researchers auditing AI agents pre-deployment, AI safety practitioners running authorized red-team tests, Developers building agent-based applications who want to test defenses early. Free to use.
What's new in Hackagent
Checked 3 days agoAcross 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.
- +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: August 2026
How we score →Key Features
- Prompt injection attack testing via CLI and SDK
- Jailbreaking attacks: AdvPrefix, AutoDAN-Turbo, PAIR, TAP
- Goal hijacking and tool misuse evaluation
- 11 attack techniques including FlipAttack, BoN, h4rm3l, CipherChat, PAP, Static Template
- Pre-built benchmark datasets: AgentHarm, JailbreakBench, HarmBench, AdvBench, StrongREJECT
- Additional datasets: BeaverTails, SALAD-Bench, WMDP, AIR-Bench, ToxicChat
- Custom dataset import from JSON, CSV, JSONL, TXT, or URLs
- HuggingFace dataset integration
- Modular attack engine with generator, judge, and target LLM roles
- Interactive terminal UI (TUI) with real-time progress and visualizations
- Optional cloud sync via HACKAGENT_API_KEY
- Runs locally out of the box
- CLI dependency scanner for AI agents (added July 2026)
- Supports multiple agent frameworks: Google ADK, OpenAI SDK, LiteLLM, LangChain, Ollama, vLLM
- Python SDK and CLI tool
About Hackagent
HackAgent is a free, open-source Python SDK and CLI designed for security researchers, developers, and AI safety practitioners to red-team AI agents before attackers do. It automates testing against prompt injection, jailbreaking, goal hijacking, and tool misuse—threats that traditional security tools miss. The modular attack engine uses three LLM roles: a generator that crafts adversarial prompts, a judge that evaluates success, and your target agent under test. It ships with eleven attack techniques including AdvPrefix, AutoDAN-Turbo, PAIR, TAP, FlipAttack, BoN, h4rm3l, CipherChat, PAP, and Static Template. You can pull from pre-built benchmark datasets (AgentHarm, JailbreakBench, HarmBench, AdvBench, StrongREJECT, BeaverTails, SALAD-Bench, WMDP, AIR-Bench, ToxicChat) or import your own from HuggingFace, files, or URLs. Everything runs locally out of the box, with optional cloud sync via an API key. An interactive terminal UI shows real-time progress and visualizations. As of July 2026, a CLI dependency scanner for AI agents extends its utility beyond attack simulation. Supports Google ADK, OpenAI SDK, LiteLLM, LangChain, Ollama, and vLLM frameworks. Compared to Garak or PyRIT, HackAgent bundles a wider assortment of attack methods and integrations. It's for pre-deployment security evaluation, not runtime monitoring.
Behind the Verdict
HackAgent stands out as a comprehensive, open-source toolkit for AI agent security. Its modular architecture with separate generator, judge, and target roles gives you granular control over attack testing. The eleven attack techniques span classic and research-backed methods, including PAIR, TAP, AutoDAN-Turbo, and CipherChat, so you can simulate real-world adversarial behavior. The inclusion of multiple benchmark datasets (AgentHarm, JailbreakBench, HarmBench, AdvBench, StrongREJECT, etc.) means you can quickly run standardized evaluations without curating your own data. If you have custom needs, you can import datasets from JSON/CSV/JSONL/TXT or HuggingFace, making it flexible for proprietary agents. The terminal UI with real-time visualizations is a nice touch for monitoring long-running attacks. The July 2026 addition of a CLI dependency scanner for AI agents broadens its appeal—it's not just an attack simulator but also a hygiene tool. It integrates with Google ADK, OpenAI SDK, LiteLLM, LangChain, Ollama, and vLLM, covering most common stacks. However, it's a CLI/SDK-only tool with no web dashboard, so less technical users will find it a barrier. It's also strictly for pre-deployment testing—not for runtime monitoring. You must have explicit permission before testing any system, and responsible use is emphasized. If you're a developer or security professional building or evaluating AI agents, HackAgent offers a practical, free way to harden your systems. It may not be as polished as commercial alternatives like Garak or PyRIT, but its open-source nature and breadth make it a valuable addition to your toolkit.
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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 new AI agent using OpenAI SDK is about to launch. The researcher wants to test prompt injection and jailbreaking before deployment.
Outcome: They install HackAgent via pip, configure a target agent with the OpenAI SDK, select a dataset like JailbreakBench, and run PAIR and TAP attacks. Within minutes they see a TUI showing attack progress and identify vulnerabilities to fix.
An agent built with LangChain needs robustness testing against goal hijacking and tool misuse.
Outcome: Engineer uses HackAgent's CLI to run against their LangChain agent, imports custom datasets, and generates a report (via CLI output) to share with the team, highlighting attack success rates and weak spots.
The team wants to automatically red-team every new agent build in the pipeline.
Outcome: Engineer adds a HackAgent step to the CI pipeline, using a script that runs a quick attack suite on each candidate agent. Failed attacks fail the build, preventing vulnerable agents from shipping.
Use Cases
- Automate red-teaming of AI agents using prompt injection and jailbreaking attacks
- Evaluate the robustness of custom AI agents against goal hijacking and tool misuse
- Integrate security testing into CI/CD pipelines for agentic AI systems
- Compare the security of different AI agent frameworks using standardized benchmarks
- Generate reproducible security reports for compliance or audit purposes
- Scan AI agent dependencies for known vulnerabilities before deployment (July 2026)
Limitations
- HackAgent is designed for authorized security testing only.
- It requires technical expertise to set up and use, as it is a CLI/SDK tool with no web interface.
- The free, open-source version works locally; optional cloud sync is available via an API key.
- The tool is not a runtime monitor—it's for pre-deployment testing.
- It does not include a graphical dashboard.
as of 2026-08-20
Verification history
We have re-verified Hackagent 6 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-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
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Hackagent's pricing actually pencils out — and where peers do it cheaper.
HackAgent is free and open-source, making it a budget-friendly choice for independent researchers and small teams. Unlike commercial tools like Garak or PyRIT, there are no licensing fees. The main cost is the LLM API usage for running attacks, which is variable. For teams needing enterprise support or a managed service, commercial alternatives might be more suitable despite the cost.
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 technical user comfortable with Python, initial setup (install via pip and run a basic attack) can be done in under 10 minutes. Configuring a custom agent or importing datasets might take 30-60 minutes. Non-technical users will face a steeper learning curve.
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: You can replicate similar attack scenarios (prompt injection, jailbreaking) using HackAgent's datasets and techniques; migrate by adapting your test scripts to HackAgent's SDK/CLI commands.
- →From PyRIT: HackAgent's modular engine allows you to define similar attack workflows; you can reuse your benchmark datasets and port them via HuggingFace or file import.
- ↗To Garak: If you need a more established tool with broader community support, you can export your findings and replicate tests using Garak's dataset and attack modules.
- ↗To PyRIT: For teams already using PyRIT, you can migrate by recreating attack scenarios using HackAgent's CLI and datasets, then adopting a hybrid approach.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Hackagent
Common stack mates teams adopt alongside Hackagent, with the specific reason each pairing earns its keep.
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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