AI red teaming is quickly becoming an essential security practice. As organisations deploy LLMs in production, they’re realising that traditional security can’t catch AI-specific risks like prompt injection, jailbreaking, or bias exploitation.
Thankfully, open-source tools are democratising AI red teaming, making sophisticated testing accessible without massive budgets or deep expertise.
This blog reviews the best open-source AI red teaming for 2025, focusing on projects that deliver real results for security professionals.
Why use open source LLM red teaming tools?
Open-source AI red teaming tools offer several advantages for teams building their first AI testing programmes.
Cost-effective
Some commercial AI security platforms can be costly. Open-source tools let you start testing immediately, making them ideal for smaller teams or those proving ROI before investing in enterprise solutions.
Transparent and community-driven
With open-source projects, security teams can inspect the code, understand exactly what’s being tested, and reduce compliance risks. At the same time, community-driven development ensures tools quickly incorporate new attack techniques and research.
Easy to integrate
Most open-source tools offer flexible APIs and customisation, making it simple to embed AI red teaming into CI/CD pipelines or MLOps workflows without vendor lock-in.
Best open source AI red teaming tools in 2025
OpenAI Evals
Maintainer: OpenAI
What it does: OpenAI Evals is a framework for benchmarking and structured evaluations of LLM behaviour, with pre-built tests for safety, accuracy, and alignment. Strong at identifying consistency issues and deviations from expected behaviour, and flexible enough for custom domain-specific tests.
Pros: Well-documented, extensive built-in test library, easy to extend, good for compliance reporting.
Cons: More evaluative than adversarial; requires some Python knowledge.
Garak
Maintainer: NVIDIA (community-driven)
What it does: Garak is an adversarial testing toolkit with 100+ attack modules, from prompt injection to data extraction. Designed for security-first workflows, it automates vulnerability scanning and maps findings to AI security frameworks with detailed reporting.
Pros: Large attack library, automated vulnerability scanning, strong reporting, updated frequently with new attack techniques.
Cons: Can produce false positives; requires tuning for production; steep learning curve for advanced configs.
ARTKIT
Maintainer: Research community
What it does: An open-source framework for automated LLM red teaming that simulates multi-turn attacker–target interactions. ARTKIT generates adversarial prompts, chains attacks across multiple steps, and analyses responses to test whether models can be manipulated into unsafe behaviour.
Pros: Supports complex, realistic jailbreak scenarios; combines automation with human-in-the-loop testing; flexible pipeline design.
Cons: Requires setup and tuning; still maturing compared to more established security toolkits.
Harness
Maintainer: Microsoft
What it does: Harness combines evaluation frameworks with attack surface mapping to assess how generative AI systems interact within larger enterprise architectures. Strong for security teams that need to understand real-world deployment risks.
Pros: Comprehensive attack surface analysis, enterprise-grade documentation, and good integration with Microsoft tools.
Cons: Newer project with smaller community; skewed toward Microsoft-centric environments.
H3: SecEval
Maintainer: Stanford University
What it does: SecEval provides structured security evaluation datasets and benchmarks to measure AI robustness under attack. Its academic grounding makes it well-suited for compliance, audits, and comparative reporting.
Pros: Research-validated datasets, standardised benchmarks, strong for audits and compliance.
Cons: More evaluation-focused than adversarial; slower to adopt cutting-edge attack methods.
How to choose the best AI red teaming tool for your needs
Selecting the right tool depends on several factors:
- Define your focus: Do you need jailbreak testing, adversarial security scanning, or compliance-ready evaluations?
- Match to team expertise: Tools like Garak and AutoJailbreak need more security know-how; OpenAI Evals is easier for beginners.
- Plan integration early: For DevOps workflows, prioritise tools with strong API/CI support. For compliance-heavy contexts, use tools that generate audit-ready outputs.
- Layer your approach: No single tool covers everything. Combine automated scanners (e.g., Garak) with specialised jailbreak testers (e.g., AutoJailbreak) for better coverage.
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