Rivac LabsAI/LLM Security Red-Teaming
A deployed LLM or agent is a new attack surface, and the exploits that matter — prompt injection, jailbreaks, data exfiltration through crafted inputs, tool-use abuse — don't show up in a standard penetration test built for traditional web applications. We run adversarial testing specifically designed for AI systems, attempting to break your models and agents the way a real attacker or a malicious user would, across the specific ways your system is actually deployed and integrated. Every finding comes with a reproducible attack case and a concrete remediation recommendation, not just a severity score. Clients use this to close AI-specific security gaps before an attacker — or a curious user posting screenshots online — finds them first.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Map the AI system's attack surface — prompts, tool access, data sources, and integration points — before testing begins
- 2
Run adversarial red-team campaigns for prompt injection, jailbreaking, data exfiltration, and tool-use abuse specific to your deployment
- 3
Reproduce and document each successful exploit with a clear proof-of-concept so engineering can verify and fix it
- 4
Review remediations with your team and re-test the patched system to confirm the vulnerabilities are actually closed
What You Get
Deliverables from this engagement
- Full AI/LLM red-team findings report with reproducible exploit cases
- Risk-ranked vulnerability list with remediation recommendations
- Re-test verification confirming fixes close the identified gaps
- Attack surface map of the tested AI system for future reference
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about AI/LLM Security Red-Teaming