Promptfoo vs Garak for LLM Red-Teaming: Which One Belongs in CI?

Why Is Comparing Promptfoo and Garak Harder Than It Looks?

Most roundups of AI security tooling, including OX Security's survey of the category, group Garak and Promptfoo under the same "AI red-teaming" label and move on. That framing misses the actual decision engineering teams face: these tools solve adjacent but distinct problems, and only one of them is built to live inside a CI pipeline.

Garak is a vulnerability scanner descended from security research practice. It probes a model, generates a report, and expects a human to read it. Promptfoo is a config-driven eval and redteam framework built to run the same way a test suite runs: on every pull request, with a pass or fail result at the end. The real question isn't which tool surfaces more jailbreaks. It's which one can run unattended, repeatedly, without generating so much noise that the team starts ignoring it.

The comparison below lays out the decision axes side by side, then walks through each tool's architecture, strengths, and limitations before getting to the part that actually matters for most teams: what survives contact with a real CI pipeline.

Promptfoo vs Garak at a Glance

Promptfoo is built around YAML configs and assertion-based pass/fail checks, which is what makes it CI-native. Garak is built around a probe-and-detector architecture meant for point-in-time scans, with no built-in gate semantics. The table below summarizes the structural differences.

Dimension	Promptfoo	Garak
Architecture	Config-driven eval/redteam framework	CLI-based vulnerability scanner
Config Format	YAML	CLI flags and probe/detector modules
CI Integration	Native (GitHub Actions examples, exit codes)	Not native; output is a report for manual review
Primary Use Case	Continuous redteam gating alongside regular evals	Periodic deep vulnerability assessment
Maintenance Burden	Config and plugin upkeep, similar to test suite maintenance	Tracking upstream probe releases and new attack categories
Panel Score	8.5/10 (TopReviewed AI panel)	Not in catalog; GitHub repo and NVIDIA's developer blog are primary documentation

Promptfoo's score comes from the TopReviewed AI panel's review of six criteria including CI/CD integration and red team plugin coverage. Garak has no catalog record, so its documentation trail runs through its open-source GitHub repository and NVIDIA's own publications, since NVIDIA backs the project.

What Is Garak and Who Is It Built For?

Garak

Garak is an NVIDIA-backed, open-source LLM vulnerability scanner. It's CLI-driven and designed to probe a model under test for jailbreaks, prompt injection, toxicity generation, hallucination, and a growing list of other failure categories tracked by its research community.

Architecture: Probes, Detectors, and Generators

Garak's design splits cleanly into three pieces. Probes generate adversarial inputs designed to trigger a specific failure mode. Detectors classify whether the attack actually succeeded against the model's response. Generators abstract the model under test, so the same probe library can run against different backends without rewriting the attack logic.

The strength here is breadth. Garak's probe library grows through active contributions from the open-source community and NVIDIA's own security research, which means new attack categories tend to show up relatively quickly after they're discovered in the wild. For a team running a pre-launch security audit, that accumulated taxonomy is hard to replicate by hand.

One Honest Limitation

Garak was designed for point-in-time audits and research reports, not lightweight repeated runs against a model that changes every week. The output is a scan report meant for human review. There's no native pass/fail gate semantics, so plugging it into CI means someone has to read the report after every run and manually decide whether the build should proceed. That doesn't scale across frequent pull requests.

Pick Garak If:

• You need a deep, one-time or periodic vulnerability assessment ahead of a launch or compliance review
• You have a security or research team with the bandwidth to interpret scan output, not just a binary signal
• You are not trying to block merges based on the scan result

What Is Promptfoo and Who Is It Built For?

Promptfoo

Promptfoo is a config-driven eval and redteam framework built on YAML, designed from the start to run as an automated gate alongside a team's regular test suite rather than as a standalone audit tool.

YAML Config and the Plugin/Strategy System

Promptfoo's redteam command generates adversarial test cases through a plugin and strategy system: plugins define attack categories (prompt injection, PII leakage, jailbreak attempts), and strategies define the generation techniques used to produce those cases. The result runs as structured eval cases with explicit pass/fail assertions, the same mechanism Promptfoo already uses for regular prompt evaluation.

That shared config format is the real advantage. A team already using Promptfoo to eval prompt quality doesn't need to adopt a second tool for security testing, it's the same YAML files and the same CI step. Promptfoo also ships with built-in CI integrations, including GitHub Actions examples and standard exit codes, which makes failing a build on a redteam regression a straightforward wiring job rather than a custom integration project.

One Honest Limitation

Promptfoo's plugin and strategy coverage is narrower than Garak's accumulated probe library. Teams chasing maximum attack surface breadth, especially around newer or more obscure jailbreak techniques, may find gaps that Garak's community-driven probe set has already closed.

Pick Promptfoo If:

• You want red-teaming to run on every pull request the way a standard test suite does
• You need a clear pass/fail signal instead of a report someone has to read and interpret
• Your team already uses config-based evals and wants security testing in the same workflow

Which One Actually Survives Being Wired Into a CI Pipeline?

Signal vs Report: Pass/Fail Gates vs Human-Readable Scans

Promptfoo survives CI wiring because its output is a binary signal by design. Garak's output format assumes a human reads the scan after the fact, which means bolting it into CI requires someone to manually parse results and decide pass or fail on every run, a step that doesn't scale once pull requests are landing daily.

This is the structural gap that generic "AI security tools" listicles skip over. A tool can have excellent attack coverage and still fail as a CI gate if its output format assumes human triage. Promptfoo's assertion-based structure, where each redteam test case resolves to pass or fail, is what a CI pipeline actually needs to block a merge automatically.

The Cost of False Positives at CI Speed

False positive tolerance differs sharply by context. A periodic research scan can tolerate noise because a human triages the findings before anything ships. A CI gate that cries wolf on every pull request gets disabled or quietly ignored within a few sprints, the same pattern that happens with flaky unit tests.

Teams iterating quickly on top of open models like Llama, or serving models locally through Ollama, tend to change prompts and model versions often enough that they need fast, low-noise signals more than exhaustive attack coverage on every single commit. That's the environment Promptfoo's pass/fail design was built for.

How Much Maintenance Does Each Tool Actually Require?

Probe Library Upkeep vs Config Maintenance

Garak's value compounds with an actively maintained, current probe library, which means someone on the team needs to track upstream releases and new attack categories as they land. Skip that upkeep and the scan quietly drifts out of date against the threat landscape it's meant to cover.

Promptfoo's maintenance burden shifts somewhere more familiar: YAML config files and plugin selection, closer to maintaining a test suite than maintaining a dedicated security tool. Teams that already treat their eval configs as living artifacts, updating them as the product changes, will find this burden lighter than tracking a separate scanner's release cycle.

Team Skillset Required

Garak assumes some security research literacy to interpret findings correctly, distinguishing a genuine vulnerability from a detector false positive isn't always obvious from the report alone. Promptfoo assumes familiarity with eval and test workflows, a skillset that's often already present on teams running experiment tracking through tools like MLflow or monitoring production errors with Sentry.

There's also an observability overlap worth noting. Teams already piping test and eval signals into Grafana or Honeycomb dashboards will find Promptfoo's structured pass/fail output easier to route into existing alerting than Garak's narrative scan reports, which weren't designed with downstream automation in mind.

Can You Run Both Promptfoo and Garak Together?

Yes, and the common pattern splits them by cadence rather than by coverage: Garak runs periodically, quarterly, pre-launch, or after a security incident, for deep audits, while Promptfoo runs continuously in CI to gate every pull request.

This division of labor mirrors how security teams already separate full penetration tests from automated scanning in other parts of the stack, the same logic that keeps cloud security platforms like Wiz running continuous checks while deeper manual reviews happen on a slower schedule. Running both tools avoids the false choice between "maximum attack coverage" and "CI compatibility". Each tool does the job it was actually built for, and neither has to compromise its design to cover the other's use case.

How Do You Decide Which Tool to Start With?

Three questions narrow this down quickly, and most teams can answer all three in under five minutes of discussion.

Do you need a pass/fail CI gate this week, or a one-time deep audit before a launch? If the answer is "this week," start with Promptfoo. If it's "before we ship in a month," Garak's deeper scan is the better first move.

Does your team have security research bandwidth to read and triage scan reports, or do you need automated binary signals? Teams without a dedicated security researcher on staff tend to get more sustained value from Promptfoo's structured pass/fail output than from a report that needs expert interpretation.

Is your LLM app's prompt or model surface changing weekly, or is it relatively stable with infrequent high-stakes releases? Frequent iteration favors Promptfoo running in CI. A stable surface with occasional major releases favors Garak as a periodic audit layered in before those releases ship.

For a team deciding where to put the first dollar and the first engineering hour: wire Promptfoo into the pipeline this sprint so redteam checks run on every pull request like any other test. Add Garak as a quarterly or pre-launch supplement once that CI gate is stable and the team has bandwidth to read a deeper report. Starting with the audit tool and bolting CI on later almost always stalls, because no one owns the manual triage step once pull requests start stacking up.