LLMArmor vs Promptfoo: Static Security vs LLM Evaluation

Promptfoo is an open-source LLM testing and evaluation framework. It runs assertions against LLM outputs to verify that prompts produce correct, safe, and consistent responses. LLMArmor scans your Python source code for OWASP LLM Top 10 security misconfigurations before your app runs.

The two tools live at different layers of the development lifecycle.

At a glance

Dimension	LLMArmor	Promptfoo
Primary purpose	Static code security analysis	LLM prompt evaluation and testing
Approach	Analyzes Python source files	Runs prompts and evaluates outputs
When it runs	At commit / CI time (pre-deploy)	During development and CI, against live models
What it needs	Python source files	LLM API access + test cases (YAML/JSON)
Standards alignment	OWASP LLM Top 10	Custom assertions + red-team plugins
Red-teaming	Static pattern detection	Dynamic adversarial prompt generation
Output	SARIF, JSON, Markdown, grouped terminal	HTML report, JSON, CI pass/fail
SARIF / GitHub Code Scanning	✅ Built-in	❌ Not natively
Cost per run	Free — zero API calls	Incurs LLM API cost per test case
License	MIT	MIT

What Promptfoo does well

Promptfoo excels at evaluating the quality and safety of LLM outputs. You define test cases with expected outputs or assertions (e.g., “the response should not contain PII”, “the response should answer the question correctly”), and Promptfoo runs them against one or more models, giving you a comparison matrix.

It also has a red-team mode that generates adversarial prompts to test whether your system prompt or guardrails hold up against jailbreaks, prompt injection attempts, and policy violations.

This makes Promptfoo ideal for:

A/B testing different prompts or models
Regression testing when you change a system prompt
Verifying that safety guardrails work as intended
Model selection and benchmarking

What LLMArmor does well

LLMArmor answers the question: “does my code introduce security vulnerabilities?” It finds patterns like:

User-controlled input interpolated directly into LLM messages (f"You are {user_role}")
LLM API keys hardcoded in source files
Tainted LLM outputs fed into eval(), subprocess, or SQL queries
Agent tools with overly broad permissions or disabled human-in-the-loop approval
Missing max_tokens on API calls (unbounded cost exposure)

These are code-level issues that exist regardless of how a model responds. Promptfoo cannot find them because it evaluates model behavior, not source code.

Overlap: security testing

Both tools address security, but from different angles:

Promptfoo red-team: probes whether the running system responds safely to adversarial inputs
LLMArmor: finds the code patterns that make the system vulnerable in the first place

For example, if a developer writes messages = [{"role": "system", "content": f"You are {user_input}"}], LLMArmor flags this as an LLM01 prompt injection risk immediately in CI. Promptfoo might later confirm this is exploitable by generating attack prompts, but LLMArmor catches it earlier and cheaper.

When to choose LLMArmor

You want security checks in CI that run without API cost
You need SARIF output for GitHub Code Scanning dashboards
You’re auditing code for OWASP LLM Top 10 compliance
You want to find misconfigurations before writing any test cases
You need fast feedback (seconds, not minutes)

When to choose Promptfoo

You want to evaluate and compare prompt quality across models
You’re regression-testing a system prompt after changes
You need to verify that guardrails block specific attack patterns at runtime
You want a visual report comparing model outputs
You’re doing model selection or benchmarking

Recommendation

These tools address different layers and work well together:

Use LLMArmor in CI to block code-level security misconfigurations on every pull request.
Use Promptfoo during prompt engineering and before major releases to verify runtime safety and output quality.

Get started with LLMArmor Install LLMArmor from PyPI and run your first scan in under a minute.