Best AI Red Teaming Tools for LLMs: A Practitioner's Comparison

The best AI red teaming tools for LLMs are not a solved category. Three years into production LLM deployments, the tooling has finally matured past toy scripts, but the gap between what automated scanners catch and what a skilled human finds is still real. This guide covers the frameworks that matter — Garak, PyRIT, Promptfoo, and Giskard — with enough technical specificity to choose the right one for your architecture, not just the one with the best landing page.

The scope here is intentional: prompt injection, jailbreaks, system-prompt exfiltration, data leakage, and RAG poisoning. Two of those have their own writeups here: system prompt extraction and the RAG prompt injection attack surface. Model extraction and training-data extraction are adjacent problems that require separate tooling and are not addressed below.

The four tools that actually ship

Garak (NVIDIA, Apache 2.0) is the closest thing to a standard LLM vulnerability scanner. It probes models for hallucination, data leakage, prompt injection, misinformation, toxicity, and jailbreak susceptibility across 15+ specialized probe modules — from encoding-based injection to DAN-style attacks to malware-generation attempts. Garak works against OpenAI, Hugging Face, AWS Bedrock, and Replicate endpoints out of the box. Each probe runs primary and extended detectors and outputs detailed JSONL reports, so findings are structured and parseable from the start. The right use case: quick coverage sweeps across known vulnerability classes, especially when you need to scan a third-party model before integrating it. The limitation is that Garak is a scanner, not an orchestrator — it doesn’t chain attack turns or adapt based on intermediate model responses.

PyRIT (Microsoft, MIT) is the orchestration layer that Garak isn’t. Rather than a fixed attack library, PyRIT gives you building blocks for constructing multi-turn adversarial attack sequences in Python. The framework is built around the idea that meaningful attacks against production LLMs — particularly agentic ones — require feedback loops: send a probe, inspect the response, adjust the next input, repeat. PyRIT integrates with Azure AI Studio and Hugging Face and is the right choice when your threat model involves complex multi-step scenarios rather than single-turn probe patterns. The cost is engineering time: PyRIT requires Python proficiency to use effectively, and building a useful attack pipeline on top of it takes real work.

Promptfoo is the tool most likely to actually run in your CI pipeline. YAML-configured, it exposes a built-in OWASP LLM Top 10 preset that maps test results directly to the OWASP LLM Top 10 framework — all ten vulnerability classes covered, from prompt injection (LLM01) through system prompt leakage (LLM07) to RAG-layer embedding weaknesses (LLM08). It has first-class GitHub Actions support and is fast enough to block pull requests without adding painful latency to the development loop. Where it falls short is depth at the retrieval and agentic layers — it tests what goes in and what comes out, but it doesn’t model tool-call abuse or multi-agent coordination failures well. For a thorough breakdown of what prompt injection looks like at the application layer, the offensive AI coverage at aisec.blog goes deeper than most.

Giskard occupies a specific niche: RAG pipeline security. If your application involves document retrieval — LangChain, LlamaIndex, Hugging Face pipelines — Giskard is the tool best positioned to surface retrieval accuracy failures and knowledge-base contamination. It doesn’t do much outside the retrieval layer, but within that scope it outperforms the general-purpose scanners. Worth adding to your stack if RAG poisoning is in your threat model and Promptfoo’s RAG coverage feels thin. For Garak specifically, see our longer Garak review.

Side by side

	Garak	PyRIT	Promptfoo	Giskard
Maintainer / licence	NVIDIA, Apache 2.0	Microsoft, MIT	Promptfoo, MIT	Giskard, Apache 2.0
Interface	CLI + probe plugins	Python framework	YAML config + CLI	Python + scan report
Attack model	Fixed probe library	You compose orchestrations	Plugin presets	RAG-specific test generation
Multi-turn	Thin	First-class	Limited	No
Runs in CI unattended	Yes (JUnit XML)	Needs engineering	Yes (GitHub Actions)	Yes
Application vs model surface	Model endpoint	Either	Application	Retrieval pipeline
Custom attack authoring	Write a probe class	Native	YAML plugin or custom provider	Custom detectors
Typical first-run effort	Minutes	Days	Hours	Hours
Weakest at	Detector false positives, static corpus	Time-to-first-result	Agent and retrieval depth	Anything outside RAG

The row that decides most choices is the second-to-last one. Effort is not a soft cost here: a tool nobody finishes wiring up produces zero findings, and three of these four will happily consume a sprint. Licence terms and what each project costs to run inside your own infrastructure are a separate axis from features, covered with Meta’s Purple Llama safeguards in open source LLM security tools: licensing and cost.

If you are looking for a Promptfoo alternative

Two different reasons people go looking, with two different answers. If the problem is that Promptfoo’s attack library feels shallow against a bare model, Garak has the broader named-technique corpus and needs no config file to produce a first result. If the problem is that Promptfoo cannot express the attack you need — anything adaptive, multi-turn, or agentic — no YAML-configured tool will fix that, and PyRIT is the honest replacement, at the cost of writing Python. Giskard is not a Promptfoo alternative; it is a different layer that sits alongside one.

Whichever you pick, fix the dataset and the judge before quoting a number from it. Which jailbreak benchmark and scoring method you run against moves reported success rates further than the choice of harness does.

Mapping tools to the OWASP LLM Top 10

The OWASP LLM Top 10 is the practical scoping framework for LLM red team engagements. No single tool covers all ten classes with equal depth:

Vulnerability Class	Best Automated Coverage
LLM01 Prompt Injection	Garak, Promptfoo
LLM02 Insecure Output Handling	Promptfoo (output assertion)
LLM03 Training Data Poisoning	Promptfoo (behavioral analysis)
LLM06 Sensitive Information Disclosure	Garak, Promptfoo
LLM07 System Prompt Leakage	Promptfoo (prompt extraction plugins)
LLM08 Vector and Embedding Weaknesses	Giskard, Promptfoo

LLM04 (Model Denial of Service), LLM05 (Supply Chain), and LLM10 (Model Theft) are not meaningfully testable with any of the tools above — they require infrastructure-level or supply-chain-specific tooling respectively. MITRE ATLAS provides technique mappings for the infrastructure side of these risks.

Promptfoo’s full OWASP LLM Top 10 coverage documentation is one of the more honest vendor pages in this space: it names what each plugin tests and where gaps exist.

What automated tooling misses

Every tool above runs a fixed or semi-adaptive payload library against the model under test. That catches what’s already been categorized. It misses:

• Context-specific trust escalation — where an attacker abuses the model’s understanding of your application’s business logic, not generic jailbreak patterns
• Multi-agent lateral movement — indirect prompt injection via a poisoned tool output that hijacks a downstream agent; none of the tools above model this reliably
• Novel encoding attacks — creative Unicode, homoglyph, or tokenization-edge-case payloads that aren’t in any scanner’s library yet. The known members of that family are catalogued in payload splitting and encoding jailbreaks; the scanners cover the named ones and nothing past them
• Whatever landed last month — every probe corpus lags the research. The Still Works? tracker is the per-quarter view of which technique classes are currently live against which model families

Automated tooling sets the floor. For applications where a successful attack causes real damage — financial transactions, healthcare data access, agentic systems with write permissions — supplement with human red teaming against the production application context. The defensive countermeasures side of this problem is documented at guardml.io if you’re building layered controls.

Choosing a starting point

If you have no automated LLM security testing today: start with Promptfoo in CI. Configure the OWASP LLM Top 10 preset and block on critical findings. The false positive rate is manageable and the setup time is measured in hours, not weeks.

If you’re testing a third-party model before integration: Garak gives you the fastest broad-coverage sweep with the least configuration overhead.

If your threat model involves multi-turn attacks or agentic flows: PyRIT gives you the orchestration primitives, but budget engineering time to build the actual attack sequences.

If RAG is your primary attack surface: Giskard.

All four tools are open-source and actively maintained. None of them replace a human red team engagement for high-stakes deployments — but all of them catch findings that manual testing at developer speed will miss. Scoping that human engagement is covered in scoping an AI red-team engagement.

Related across the network

• AI Red Teaming Hub: Your Guide to Offensive AI Security — aisec.blog
• PyRIT Review: Microsoft’s AI Red Teaming Framework — aisecreviews.com