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Compare Promptfoo and Garak for LLM security testing. Learn how dynamic attack generation differs from curated exploits, and when to use each tool. | /img/blog/garak/promptfoo-vs-garak.jpg | 2025-06-26 | [ian] | [tool-comparison red-teaming garak] |
As LLM applications move into production, security teams face a critical challenge: how do you systematically identify vulnerabilities before attackers do?
Two open‑source tools have emerged as popular choices for LLM red teaming, each taking a fundamentally different approach to the problem.
Feature | Promptfoo | Garak |
---|---|---|
Approach | Dynamic, application‑specific attack generation | Curated library of research‑backed attacks |
Best For | Custom applications, RAG systems, agents | Vulnerability scanning |
Attack Generation | AI‑powered, contextual | Static with "buff" perturbations |
CI/CD Integration | Native GitHub Actions & CLI | Audit‑style runs via CLI |
RAG Testing | Specialized RAG security suite | General prompt‑injection checks |
Agent Security | RBAC, tool misuse, API fuzzing | Limited |
Compliance Mapping | OWASP, NIST, MITRE, EU AI Act | AI Vulnerability Database |
License | MIT | Apache‑2.0 |
:::tip Key Insight Promptfoo discovers vulnerabilities unique to your application through AI‑generated attacks, while Garak focuses on known LLM exploits. :::
Promptfoo approaches LLM security from an application‑developer perspective. Rather than treating the model as an isolated component, it tests complete LLM systems—including RAG pipelines, agent architectures, and API integrations. The tool dynamically generates thousands of attack variations tailored to your specific application context, much like a fuzzer that understands natural language.
Garak (Generative AI Red‑teaming & Assessment Kit) is developed with support from NVIDIA and provides a library of pre‑defined attacks based on academic research and documented vulnerabilities. Security researchers can run Garak against LLM endpoints to check for known weaknesses.
Both projects are actively maintained. Promptfoo uses an MIT license and is adopted by teams at Shopify, Discord, and Microsoft. Garak uses Apache 2.0 and integrates with NVIDIA’s NeMo Guardrails.
Garak interface:
Promptfoo interface (Promptfoo has a CLI too, but here is its web view):
Promptfoo uses AI models to generate attacks specific to your application. When you run its generator, it analyses your system prompts, understands your use‑case, and creates thousands of contextually relevant attacks.
:::info Example in Action If you’re building an HR chatbot, Promptfoo might craft attacks like:
These attacks are tailored to your specific domain and security policies. :::
This approach effectively performs intelligent fuzzing of the prompt space and adapts to custom guardrails, industry contexts, and unique application logic.
Garak maintains a library of static, research‑backed attack prompts organized into 20 categories. These include well‑documented exploits such as “DAN” jailbreaks, encoding tricks to bypass filters, and prompts designed to extract training data. “Buffs” provide basic perturbations via paraphrasing, encoding, or translation, and experimental modules attempt broader algorithmic jailbreaking.
For fundamental model‑layer vulnerabilities—such as toxic content or encoding‑based bypasses—both tools provide coverage. Promptfoo generates variations specific to your policies, whereas Garak applies known exploits.
Retrieval‑Augmented Generation introduces new failure modes beyond simple prompt injection. Promptfoo treats the entire RAG pipeline as the attack surface (RAG plugin):
Garak checks model behavior but does not dive into RAG‑specific issues.
Modern agentic apps introduce risks such as SSRF, BOLA/BFLA, and command escalation. Promptfoo’s agent suite automatically attempts multi‑turn escalations, memory poisoning, and API parameter tampering. Garak remains focused on single‑turn model responses.
Promptfoo was built for CI/CD pipelines):
# In your CI pipeline
npm install -g promptfoo
promptfoo eval --config security-tests.yaml
Teams often run a small subset on every pull request, then a full scan nightly.
Garak can be used for one-off audits:
python -m garak --model my-api --probes all --report audit-$(date +%Y%m%d)
Promptfoo offers support for REST HTTP interfaces, Python scripts (and other languages), helpers for LangChain, browser automation, and stateful test flows. Garak provides an HTTP REST interface.
Promptfoo maps results to OWASP, NIST RMF, MITRE ATLAS, and the EU AI Act, producing ready‑to‑share reports.
Garak can optionally push findings to the AI Vulnerability Database and compare guardrail effectiveness via NeMo integrations.
For organizations evaluating these tools at scale, enterprise features and support can be a key decision point. While both Garak and Promptfoo are open-source, Promptfoo has an Enterprise edition.
Available in Promptfoo Enterprise:
The enterprise version also includes a web-based dashboard where teams can:
:::info Promptfoo offers ISO 27001 compliance and enterprise support. Garak is pure open-source with community support. :::
Choose Promptfoo when you need continuous CI/CD coverage, custom attack generation, and deep RAG/agent testing.
Choose Garak when you primarily need to validate against known exploit classes and export findings to community vulnerability trackers.
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