Ai red teaming arena

Analytics for duels
An actual duel
final comparision for models after each duel to see what fits based on the use case

Inspiration

AI systems can fail in ways traditional tests miss — through prompt injection, data leakage, unsafe tool use, and jailbreaks. We built Red-Team Arena to make AI security testing repeatable, measurable, and useful to development teams.

What It Does

Red-Team Arena launches adversarial red-team and defensive blue-team agents against AI applications. It records every attack, response, guardrail decision, and outcome, then converts failures into evidence-backed remediation proposals for human review.

It also provides:

Versioned security-test corpora
Model-to-tool attack-path analysis
Reproducible before-and-after testing
CI release gates
Protection against regressions and false positives

How We Built It

We used Laravel, PHP, PostgreSQL, Redis, and Docker Compose. LLM providers are integrated through a shared HTTP client, while imported test corpora are fingerprinted with SHA-256 for reproducibility.

We model risk conceptually as:

$$ R \propto \text{Reachability} \times \text{Sensitivity} \times \text{Control Gaps} $$

The platform stores complete execution traces so every finding can be connected to the exact input, model response, control decision, and remediation test.

Challenges

Our biggest challenges were keeping AI-generated attacks realistic while making results deterministic, preserving legitimate behavior while blocking attacks, and maintaining compatibility between Laravel 8 and PHP 8.5.

We also ensured optional or unavailable controls were reported honestly instead of being presented as successful protections.

What We Learned

We learned that AI security requires more than detecting harmful output. Teams need reproducible evidence, attack-path visibility, regression testing, and human approval before applying AI-generated fixes.

The result is a platform that does not simply find vulnerabilities — it helps teams prove they fixed them.