We test how your AI systems fail in production.
LLM agents making decisions. Autonomous workflows in production. AI deploying into environments where the consequences extend beyond downtime. These systems create attack surfaces that traditional security testing doesn't cover.
We test them with adversarial tradecraft against your live deployment — not against a sandbox.
Adversarial testing against live AI systems.
We simulate real adversaries targeting LLM pipelines, agent workflows, and retrieval systems. Real exploitation against your production deployment — not theoretical risk from a checklist.
- Red team simulations tailored to LLM pipelines and agent systems
- Threat modeling across agent workflows, APIs, and trust boundaries
- Adversary testing against production deployments
- Prioritized remediation specific to your architecture
Attack Surfaces Specific to LLM Systems
Prompt Injection
Direct injection through user inputs. Indirect injection via retrieved documents or external data. Jailbreaks that bypass system prompt restrictions.
Agent Workflow Exploitation
If your LLM can call tools, we test what happens when those tools are abused. Privilege escalation through agent actions. Chained operations that achieve unintended objectives.
RAG Pipeline Attacks
Data leakage through retrieval. Poisoning attacks against your knowledge base. Access control bypasses that expose documents users shouldn't see.
Data Exfiltration
Training data extraction. System prompt disclosure. PII leakage through model outputs. We find what your model knows that it shouldn't share.
API & Infrastructure
Authentication weaknesses. Rate limiting bypasses. Model endpoint enumeration. The traditional attack surface underneath the AI layer still matters.
Trust Boundary Analysis
Where does your system trust the model's output? What happens when that trust is misplaced? We map the boundaries and test what breaks when we cross them.
What you get.
Proof of Exploitation
Screenshots, payloads, step-by-step reproduction. Demonstrated impact against your live system.
Risk-Based Prioritization
Findings ordered by actual exploitability and business impact within your deployment context.
Actionable Remediation
Specific fixes for your architecture. Guidance that accounts for how your system is actually built.
Retest Included
Fix the issues, we verify the fixes. Remediation confirmation is part of the engagement.
Let's talk about your AI stack.
15-minute call to understand what you're deploying and what testing makes sense.