Top 5 Open Source AI Red Teaming Tools in 2025

Why are we red teaming AI systems?

If you're looking into red teaming AI systems for the first time and don't have context for red teaming, here's something I wrote for you.

Artificial intelligence has the world in a choke-hold. The rush to integrate large language models (LLMs) into existing pipelines and new applications has opened the floodgates to a slew of vulnerabilities. We obviously prefer a secure application situation, so AI security is quickly becoming a top priority for many organizations and users alike. Or at least most of us do - I'm sure all the hackers and malicious transgressors would beg to differ.

AI systems are notoriously vulnerable to malicious attacks, AI model misconfigurations, and data leakage. Input manipulation, such as prompt injections or base64-encoded attacks, heavily influence the outcomes of AI systems. Established tooling often comes with some level of security out of the box and makes software easier to secure due to decades of testing in those areas. However, traditional software is not enough to maintain the same standard of vulnerability management or keep up with emerging threats. We sit in a space where many companies offer services, yet relatively few make the tooling widely available. Forget making it free and open source.

If we want cybersecurity practices to take more of a foothold, particularly now that AI systems are becoming increasingly common, it's important to make them affordable and easy to use. Tools that sound intimidating and aren't intuitive will be less likely to change the culture surrounding cybersecurity-as-an-afterthought.

I spend a lot of time discussing what makes AI red teaming software good at all. You're free to just skip to the software.

Summary: red teaming for AI security

AI red teaming is a proactive and systematic process that uncovers risks and security vulnerabilities in AI systems, preferably before they hit production. In the spirit of traditional red teaming exercises, it simulates adversarial attacks and stress-tests AI models under real-world conditions. The benefits are numerous, which include:

• Evaluation of whether AI models are compliant and adhere to legal, ethical, and safety standards.
• Regulatory bodies are evolving these to match emerging threats; it is easier to check compliance with tooling that supports industry standards.
• Improving fairness by uncovering biases in training data or decision-making processes.
• Third-party AI red teaming provides unbiased evaluations of AI systems.
• Ensuring AI systems are robust and not leaking any sensitive data.

Traditional software doesn't cater to the scale or specificity of LLM response. As a result, exposing security risks can be time-consuming. Security teams are better empowered with appropriate AI security tooling.

In tandem with blue teaming goals

Red-teaming efforts exist to uncover security vulnerabilities so that blue team operations are well-informed to build a protected system and on where they should secure AI. Much like how traditional red teams simulate high-stress environments, automated AI red teaming models attacker behaviors to test AI systems' limits, and at scale. Developer teams still produce reports and evaluate findings so the information is useful.

A couple of the top priorities in AI security are to protect sensitive data and stay within the confines of appropriate role-based access controls; security vulnerabilities regarding these tend to do the most damage. What constitutes harmful behavior may vary between organizations.

Organizations must consider how to protect sensitive data and users throughout an entire AI system's lifecycle. All connection points need to be secured in order to reduce risk. The threat landscape can be large and often a red team process structured with scoping and strategy incorporates this to produce the best outcomes.

Why do they need to be open source?

They don't have to be, but proponents of open-source software favor them for common reasons:

• Transparency in how the tool operates.
• Often customizable.
• Cost-effectiveness.
• Trust in community-driven choices.

In order to encourage developers to participate in a better AI security culture and prioritize cybersecurity in their projects, making tools free to use and adapt is the first step towards making that goal actionable for developers. This specific market isn't exactly flooded with tools for AI red teaming, or AI security tools in general.

What makes a good AI red teaming tool?

Software engineers look for many features, and the core goal is to expose security vulnerabilities. At Promptfoo, we have seen needs grow from solo evals for small projects to comprehensive red teaming requirements for established products.

Stating the obvious

It should go without saying, but AI security tools with the following see greater adoption:

• Great developer experience (DX). UX is important for us nerds, too. It encourages adoption and reduces organizational friction.
• Great DX makes AI security more approachable even for someone who doesn't usually run them.
• Freedom to fork and customize software to a workflow without waiting for vendor updates.

Comic credit: Errant Science

Not all open-source tools are invested in improving their user experience; this is a byproduct of focusing on feature implementation without any design experience on the team.

AI Model agnosticism

A tool should work across major large language model providers and self-hosted models to:

• Avoid vendor lock-in for stack flexibility.
• Allow comparison of multiple AI models for a deeper understanding of their behaviors, given variable training data.
• Accommodate architectures with multiple models to test them uniformly.
• Reduce dependency on any single model according to governance or internal policies.

I've come across users who swap between models when they're frustrated or even angry, going as far as to complain along with posting the inadequate results. I see no reason why this process shouldn't be automated along with the rest of the pipeline. 😁

Customization (especially for AI testing)

Designing a test suite and scenarios encourages:

• Reflect real-world risks and run simulations tailored to a specific user base (for example, developer platforms see more technically sophisticated attacks, while student platforms may see plagiarism-evasion attempts).
• Keeping pace with emerging threats. A system supporting custom plugins and strategies means you don't have to wait for a software update to support it.
• Domain specificity: healthcare platforms require medical tests that are irrelevant for a financial platform or a general chatbot.

CI/CD and automation before deploying AI systems

More involved projects will run red teams regularly. Moving from mitigating risks to preventing them comes with:

• Make red teaming a proactive part of the deployment cycle before production.
• Catch regressions early; surprises happen.
• Treat red teaming like running tests; developers already run tools like Jest or pytest.
• Test-driven prompt engineering: refine prompts until they actually pass tests.

Test-driven development is the way of life for automated red teaming, my friend. If you haven't, it'll be time to finally embrace after putting it off for who knows how long you've been actively avoiding meaning to do it.

Output scoring and reporting

Auditing and reports are a common expectation of AI security in order to measure progress and compliance. Understanding outputs is naturally a part of the process. Useful software will help you:

• Prioritise issues and triage severity through scoring.
• Measure model robustness using quantitative scoring.
• Accommodate human audits and adjustments for legal, risk, and compliance reasons.
• Share clear reports with anyone - not just red teamers.

Prompt variation and context injection

The point of AI red teaming is to generate a variety of attacks. Tiny tweaks to prompt injections can bypass guards already in place. A great tool would support:

• Prompt phrasing changes.
• Evasive behavior simulation (such as formatting or emoji tricks).
• Multi-turn testing; some jailbreaks depend on a conversation of prompts rather than just one-shot.
• Mimicry of real-world adversarial behavior by malicious actors.

AI red teaming often revolves around prompts due to the problematic nature of variability caused by all the forms natural language can take. Top-notch AI security would involve the entire context in which artificial intelligence components sit, and not just anything directly related to the AI models themselves.

Red teaming traditionally involves an expert attacking a system using various methods like any user would. The manual process is still important to include on top of any routine testing; take it into account if you want maximum security for your AI system. Inspecting attack techniques and human tweaking is also advisable.

Also...

Aside from what's on the box, I've come to appreciate when software can grow with my project without overwhelming me; this means I can easily grow with it. Even better if I'm growing inwards instead of outwards.

After building software for over a decade rebuilding a part of a stack with a similar tool feels like a waste of time. I want to spend my time creating value, and the shininess of a new tool loses lustre quickly with new tool pains. I only want to replace it when it's no longer suitable, and the longer that takes the better.

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Top open source AI red teaming tools (2025)

Note: We build Promptfoo. We include competitors and link to their docs for balance.

Bear in mind that we're looking to run red teams, not just evals. I've omitted tools that only run evals; while evals can be used as a part of a red teaming workflow (and we certainly utilise evals in our AI red teaming process), that is only part of the process in highlighting security risks from AI responses.

1. Promptfoo

You're on the Promptfoo blog, so let's get this one out of the way.

Overview: A dev-first framework for evaluating and red teaming LLMs that automatically generates attack prompts and deploys them using various strategies.

Key features

• CLI as well as a web interface
• Prompt variants, model comparisons, scoring functions
• CI/CD integrations (GitHub Actions, CLI)
• Compliance mapping: OWASP, NIST, MITRE ATLAS, EU AI Act
• Best For: Developers, ML teams, prompt engineers in production workflows

I'd use Promptfoo if I were looking to red team an app in production, especially now that I've become more familiar with enterprise-level red teaming. I prefer to use JS/TS for web projects, and I mainly do web projects, so this is already a no-brainer. Honestly, I'd use it for simpler things too just because of the JS support.

There are evolving features to address the state of the pipeline beyond the LLMs themselves, like MCP testing. MCP is a protocol for tool access and secure integrations, and I treat giving AI agency with the same level of wariness I'd have of stepping on Lego in the dark an hour after lights-off. If my LLM integration is growing I'll be looking to offload more testing.

2. PyRIT (Python Risk Identification Tool)

Overview: Microsoft's PyRIT (Python Risk Identification Tool for generative AI) uses "converters" to transform inputs and orchestrators to run single-turn and multi-turn attacks at scale.

Key features

• Scenario-based adversarial testing
• JSON output for easy analysis/logging
• Focused on harmful response detection and injection
• Best For: Security teams doing targeted risk audits

I'd pick PyRIT if I was into configuring minute details, wanted to customise raw output myself, and doing custom deep dives. It also suits Python-based projects. In fact I'd probably run it alongside Promptfoo as part of a wider AI red teaming strategy. I'd give it consideration for an academic project.

3. Garak

Overview: A red-teaming vulnerability scanner with probe-based testing, HTML reports, and AVID integration. Now maintained under NVIDIA/garak.

Key features

• Automated scanning
• Fuzzer-like input generation
• HTML reports, coverage metrics
• AI Vulnerability Database (AVID) integration
• Best For: Researchers and red teamers doing exploratory security probing

Garak's great! I'd roll with it if I was looking to quickly run some tests across known exploits, I was already running Python as a requirement, if I only prioritised CLI interaction, and rarely needed to share a report. It's quick to start, straightforward to run, and I could run scans automatically without configuring much at all. I'd also give Garak heavier consideration for an academic project.

4. DeepTeam

Overview: A relatively new addition to the AI red teaming space (first release in May 2025).

Key features

• Composable attack/test workflows
• Flexible Python SDK
• Integrates with multiple LLM backends

ConfidentAI did such a fantastic job with DeepEval I'd have high expectations for DeepTeam, but taking into consideration that the tool is in its infancy I'd consider it for simple projects. I'd expect to get started quickly and be presented with a general overview of how my models are performing, with the added bonus of implementing the most basic of guards for inputs and outputs.

5. Viper

Overview: A general red-team platform with a visual UI, integrated LLM agent, and many post-exploitation modules; suitable for traditional security teams exploring AI-augmented operations.

Key features

• Plenty of out-of-the-box tooling with a web UI
• Supports red team assessments across Windows, Linux, and macOS
• Supports network pentesting

Viper would be my go-to if I was interested in tailoring attacks across different platforms, a visual representation (that pivot graph looks great!), and network penetration.

Honorable mentions

These tools deserve some visibility; they're simply not as comprehensive as the above tools when it comes to payloads and strategies (the bread and butter of current AI red teaming practices) but have quirks worth consideration.

6. Agentic Security

Agentic Security appears to be one of the few tools focused on agentic architecture. It's designed as a safety scanner too so I'd use it for that simple purpose.

7. Woodpecker

Woodpecker by Operant AI is an open source red teaming tool for AI, Kubernetes, and APIs, first released publicly in May 2025. If your focus includes K8s and API posture, it is worth consideration.

Feature comparison table

Tool	Focus Area	Key Features	Best For
Promptfoo	LLM red teaming, regression testing	Model-agnostic evaluations; CLI and CI/CD; side-by-side comparisons; maps to OWASP, NIST, MITRE ATLAS, EU AI Act	Continuous testing and benchmarking of enterprise apps and models in pipelines
PyRIT	Offensive prompt engineering	Multi-turn orchestrators; converters (audio, image, math); seed-prompt datasets; scoring with Azure Content Safety	Red teaming for enterprise LLM deployments exerting full control over red team flows
Garak	LLM safety & exploit testing	Probe-based scanning; HTML reports with z-score grading; optional AVID integration	Comprehensive adversarial testing against prebuilt and custom models for known exploits
DeepTeam	Agent red teaming	Agent- and scenario-driven tests; Python SDK; multiple LLM backends; launched May 2025	Evaluating reasoning agents in risky or sensitive domains
Viper	General red-team platform	General red-team platform; rich web UI; integrated LLM agent; Windows/Linux/macOS	Traditional security teams exploring AI-augmented red team operations

Conclusion

Everyone has specific project requirements, and we're best served by open-source tools that do different things well. Hopefully I've shed some light on why one would pick one open-source red team tool over another.

May your efforts in securing AI be fruitful.

If you have any other questions, feel free to drop me a DM!