Adversa finds the vulnerabilities in your AI systems that traditional security tools were never built to see — at the model behavior layer, not just the perimeter.
We test what traditional pen-testers can't — the model itself, its behaviors, its training artifacts, and its agentic attack surface.
Full adversarial simulation against your production AI stack. We play attacker — crafting real prompt injection chains, jailbreak sequences, multi-agent pivot attacks, and data exfil probes. You see exactly what a motivated adversary sees.
Deep code and configuration review of your model pipeline, training data controls, inference infrastructure, and access gates. We surface insecure model serialization, broken RBAC, unprotected embeddings, and misconfigurations before attackers do.
Can your model be made to leak its training data? We run membership inference, model inversion, and extraction attacks against your deployed models — black-box, gray-box, and white-box — to quantify your privacy exposure.
Weekly briefings on emerging adversarial AI techniques, threat actor TTPs targeting LLMs, new CVEs and PoC releases, and dark-web chatter about AI vulnerabilities. You stay ahead — not behind.
Deploy CHLOE's ABP gate into your AI stack for 24/7 behavioral monitoring. Every inference is checked for injection patterns, jailbreak signatures, anomalous tool calls, and output policy violations in real time.
Your engineers are the first line of defense. We run hands-on workshops and attack simulations so your team understands how prompt injection works from the inside — and builds defenses that actually hold.
We follow a structured six-phase engagement. Nothing is left to chance — every phase has defined deliverables and a clear handoff to the next.
CHLOE's Adaptive Behavior Protocol (ABP) was engineered from first principles for adversarial AI environments. It runs at the behavior layer — not the output filter layer.
Original adversarial AI research — no vendor spin, no paywalls. If we find it, we publish it.
An attacker can inject adversarial instructions into externally-retrieved documents. When processed by a RAG-augmented LLM agent, these instructions are silently executed — including tool calls, file writes, and exfiltration commands.
We demonstrate recovery of training-set PII from GPT-4-based fine-tuned models using only 200 inference queries to the embedding endpoint — no model access required beyond a standard API key.
Adversarial instructions injected early in a conversation can remain latently active across hundreds of subsequent turns — surviving context summarization, system prompt reinjection, and standard safety guardrails.
Attackers can craft prompts that cause function-calling-enabled models to invoke tools outside their intended scope, accessing restricted APIs and data sources through the tool execution layer.
Demonstrating that fine-tunes based on publicly available poisoned datasets introduce persistent backdoor behaviors — triggered by specific token sequences — surviving further fine-tuning rounds.
Clarity up front — so there are no surprises during engagement kickoff.
We'll find them before the adversary does. Engagements typically begin within 2 weeks of scoping. Retests are always included.
security@adversa.net