See how ModelRed
stacks up
Compare features, pricing, and capabilities. See why teams choose ModelRed over manual testing, generic security tools, and building their own solution.
Why teams choose ModelRed
Four key capabilities that set us apart from alternatives
Version-Locked Testing
Only platform with versioned probe packs. Pin v2.0 to prod, v2.1 to staging, and compare results across releases.
Provider Agnostic
Test any model, anywhere. OpenAI, Anthropic, AWS, Azure, Google, and custom endpoints. No lock-in.
Detector-Based Verdicts
Dedicated LLM detectors produce reproducible, explainable pass/fail signals. Not just pattern matching.
Built for CI/CD
Native integration with GitHub Actions, GitLab CI, and more. Fail PRs on high-risk findings automatically.
Feature comparison
How ModelRed compares to alternative approaches
How ModelRed uses ModelRed
We eat our own dog food. Here's how we use our platform to test our AI features before they ship.
Featuring insights from:
ModelRed Security Team
Engineering & Product Security
Challenge
Testing our own AI features (AI probe generation, detectors) for security vulnerabilities before release
Approach
Use ModelRed in our CI/CD pipeline to run continuous security assessments on every commit
Results
- Caught 12 vulnerabilities pre-production
- Zero security incidents in 3 months
The Problem: Testing AI with AI
When we built ModelRed, we faced a unique challenge: how do you test an AI security platform that uses AI to detect vulnerabilities? We needed to ensure our own AI features—like the AI probe generator and LLM-based detectors—couldn't be manipulated or bypassed.
Our Solution: Continuous Self-Testing
We integrated ModelRed into our own CI/CD pipeline. Every time we push code that touches our AI features, we run a full security assessment using our most aggressive probe packs:
- Injection Pack v2.1 - Tests for prompt injection in our AI probe generator
- Jailbreak Pack v2.0 - Ensures our detectors can't be tricked into false negatives
- Exfiltration Pack v1.9 - Verifies we don't leak sensitive data through our AI responses
Real-World Catch: The Detector Bypass
In August 2024, we caught a critical vulnerability in our detector system. A specially crafted prompt could cause our jailbreak detector to return a false negative—marking a malicious prompt as "PASS" when it should have been "FAIL."
ModelRed caught this in our CI pipeline before it reached production. The PR was automatically blocked, and we fixed the issue within 2 hours. Without continuous testing, this vulnerability could have compromised every customer assessment running through that detector.
Our Current Setup
We run ModelRed assessments at three stages:
- Pre-commit hooks - Quick sanity checks (~30s) using a lightweight pack
- PR validation - Full assessment (~5 min) with all packs, blocks merge on high-severity findings
- Production monitoring - Continuous assessments every 6 hours, alerts on score drops
- • 12 vulnerabilities caught before production
- • Zero security incidents in 3 months
- • 5 minute avg assessment time in CI
- • ModelRed Score: 8.7 (our own platform)
What We Learned
Using our own platform taught us valuable lessons that shaped the product:
- Version locking is essential - Being able to pin probe pack versions per environment prevents surprises in production
- Speed matters in CI - We optimized our assessment engine to run 3x faster based on our own needs
- Clear verdicts save time - Detector-based pass/fail signals are way better than manual review of raw outputs
- Continuous > periodic - Running assessments every 6 hours catches issues faster than weekly scans
The Bottom Line
We trust ModelRed to protect our own AI platform. Every feature we ship has been battle-tested against thousands of attack vectors. If it's good enough for us, it's good enough for you.
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