AI Red Teaming · LLM Evaluation · QA Services
ValBench is a specialist quality-assurance firm for the AI era. We red-team LLM applications, benchmark model behavior against real-world abuse, and back it up with the full testing stack — functional, performance, and penetration testing.
What we do
Our mark is a white box and a black box for a reason — we test what you can see and what an attacker can't. AI safety is our specialty; classic QA is our foundation.
Adversarial testing of LLM apps, chatbots, agents, and RAG pipelines. Prompt injection, jailbreaks, data exfiltration, tool-abuse, and unsafe-output discovery — before your users find them.
Structured, repeatable evaluation of model quality: accuracy, groundedness, bias, toxicity, robustness, and regression tracking across model or prompt changes.
End-to-end functional QA for web, mobile, and API products — manual exploratory testing plus maintainable automation frameworks your team keeps.
Load, stress, soak, and scalability testing so launch day is boring. Includes LLM-specific latency and token-throughput profiling.
Vulnerability assessment and penetration testing for applications, APIs, and cloud infrastructure — the classic security layer under your AI layer.
Deep dive
Shipping an LLM feature without adversarial testing is shipping untested code paths — infinitely many of them. A ValBench engagement answers one question: what can a motivated user make your AI do?
Scope an EngagementHow we work
We map your AI surface — models, prompts, tools, data flows — and agree on rules of engagement and success criteria.
Automated adversarial suites plus expert manual probing. Every finding is reproduced, ranked, and recorded.
An executive summary for leadership and a technical annex for engineers — severity, impact, reproduction, and concrete fixes.
After remediation we re-run the full attack set and issue an updated readout you can share with customers and auditors.
Why ValBench
AI red teaming isn't a service line we bolted on — it's the reason the firm exists. Classic QA disciplines support it, not the other way around.
Engagements map to the OWASP Top 10 for LLM Applications, MITRE ATLAS, and the NIST AI Risk Management Framework — language your compliance team already speaks.
Every finding ships with a working reproduction, severity rationale, and a suggested fix. No hand-wavy "AI risk" decks.
Clear packages with defined deliverables and timelines. You know the cost and the artifact before we start.
Tell us what you're building. We'll walk through the most likely attack paths against your AI feature — no charge, no deck, just a whiteboard session.