AI Red Team Operations
We don't just test whether your model can be tricked—we prove what an attacker can actually do with it. Our operators target the entire AI ecosystem: the LLM, its system prompts, RAG and vector stores, AI agents, and the over-permissioned tool and MCP integrations behind them—chaining prompt injection into real business impact like data exposure, leaked secrets, and unauthorized actions. Every finding is mapped to OWASP LLM Top 10, MITRE ATLAS, and the NIST AI RMF your auditors recognize.
Request an AI Red Team Assessment →Most "AI red teaming" stops at getting a model to say something it shouldn't. We go further—proving what an attacker can actually reach, exfiltrate, or trigger across your organization through the AI system and everything wired behind it.
AI Red Teaming
Pressure-tests the model itself—probing for bias, harmful or unsafe content, and jailbreaks to measure how it behaves under adversarial input. Essential for safety and alignment, but it stops at the model's output.
AI Penetration Testing
Proves organizational risk by manipulating the model and the systems around it—APIs, RAG stores, agents, and tool integrations—then chaining that foothold into demonstrable impact. An attacker only has to be right once; our job is to show exactly how far that one success goes.
Most AI Risk Is Application Security in Disguise
The majority of what goes wrong in AI systems isn't exotic model math—it's classic application and API security (IDOR, SSRF, secrets in the wrong place, over-permissioned integrations) reached through a new front door: the LLM. That delivery layer is what's new; the underlying attack surface is exactly the territory our operators have tested for years. We bring battle-tested offensive depth to the AI layer on top of it.
An LLM never exists in a vacuum. We test the model, the prompts that steer it, the data it reaches, the agents and tools it can act through, and the application around it—because that's where real-world compromise actually happens.
AI Ecosystem & Infrastructure
The devops and infrastructure around the model: leaked API keys, exposed dashboards and admin tooling, MCP servers, agent-to-agent trust relationships, and the AI supply chain—often carrying old-school web vulnerabilities behind a shiny new interface.
The Model & Its Guardrails
Direct testing of the model and its defenses—prompt injection, jailbreaking, role hijacking, and classifier/guard-model bypass—plus bias and harmful-output checks. We test hosted frontier models, custom-trained models, and third-party AI APIs alike.
System & Developer Prompts
System-prompt leaking remains one of the most reliable wins in AI testing. A leaked prompt routinely exposes API keys, secret endpoints, hidden content rules, and a blueprint of exactly how the larger system is wired—fuel for every attack that follows.
RAG, Vector Stores & Data
Retrieving sensitive data from RAG and vector stores, poisoning self-training and retrieval loops, and corrupting the knowledge the model depends on. We test what your AI can read, what it should never surface, and what an attacker can quietly plant in it.
AI Agents & Tool Integrations
Over-permissioned agent tools and MCP integrations turn a single prompt injection into real-world action—reading and writing to Slack, Jira, Confluence, SharePoint, CRMs, and internal APIs. We test for excessive agency and prove what a hijacked agent can actually do.
Application & Integration Layer
Traditional web bugs with an AI twist: XSS through prompt smuggling, SSRF to internal model/vector endpoints and cloud metadata, IDOR and prompt-driven privilege escalation, malicious file upload as an injection vector, and RCE via code-execution sandboxes.
Every AI red team engagement covers the OWASP LLM Top 10, MITRE ATLAS adversarial tactics, and emerging AI attack patterns documented across industry research.
Prompt Injection (Direct & Indirect)
Crafted inputs that hijack system context, bypass guardrails, or manipulate the model into performing unauthorized actions through embedded instructions in user data or upstream sources.
Sensitive Information Disclosure
Extraction of training data, PII, system prompts, API keys, or proprietary business logic through inference attacks, membership queries, and carefully sequenced model interactions.
Insecure Output Handling
Unvalidated model outputs causing XSS, CSRF, SSRF, or code execution in downstream systems. We test every point where your app trusts LLM responses without sanitization.
Supply Chain Vulnerabilities
Risks from third-party model weights, plugins, open-source datasets, and API integrations. We test what you're inheriting from external AI dependencies.
Model Denial of Service
Crafted inputs engineered to consume excessive compute, exhaust context windows, or spike inference costs—degrading availability and driving unexpected bills.
Overreliance & Hallucinations
Testing for scenarios where blind trust in model outputs leads to critical business decisions based on hallucinated data, fabricated citations, or confidently incorrect responses.
The single highest-leverage step in any AI assessment happens before a single payload is sent. A structured threat model maps your system, scopes the engagement honestly, and turns "we should probably test the AI" into a prioritized plan—often the difference between a focused two-to-four-week engagement and a runaway one.
Architecture & Questionnaire
We work from an architecture diagram and a structured AI security questionnaire to inventory components, trust boundaries, and data flows—so testing is targeted at what actually matters instead of guessed at.
Framework-Mapped
Built on Adam Shostack's Four-Question Framework and mapped to the OWASP LLM Top 10 (2025), MITRE ATLAS, and STRIDE—so the threat model speaks the language your auditors and leadership already recognize.
Intellectually Honest
Every threat is tagged Confirmed, Assumed, or Unknown—we surface what we still need to ask rather than guessing silently. You get a clear "open questions" list instead of false confidence.
Your Deliverable
A self-contained threat-model report: component inventory, trust boundaries, a data-flow diagram, a threat register with risk ratings, prioritized countermeasures, a validation and test plan, and a ready-to-act list of open questions. It stands on its own as a board-ready artifact—and doubles as the scope and roadmap for a full AI penetration test.
A structured, operator-led engagement that works through the full AI attack surface—then proves real impact. Every phase is mapped to OWASP LLM Top 10, MITRE ATLAS, and the NIST AI Risk Management Framework so findings tie directly to the standards your auditors and leadership recognize.
1. Threat Model & Scope
We start with an architecture diagram and security questionnaire to map components, trust boundaries, and data flows. This controls scope and timeline and ensures we test what carries real risk—not just what's easy to reach.
2. Attack the Ecosystem
Infrastructure and devops around the model: exposed dashboards and admin tooling, leaked secrets and API keys, MCP servers, agent-to-agent trust, and the AI supply chain—where many of the most serious findings actually live.
3. Attack the Model & Prompts
Direct, indirect, and proxied prompt injection; jailbreaking and guardrail bypass; and system-prompt leaking that exposes secrets and the wiring of the system behind it. Operator-led—no automated scanners, no generic scripts.
4. Attack the Data & Agents
RAG and vector-store retrieval and poisoning, plus abuse of over-permissioned agent tools and MCP integrations—turning a single injection into reads and writes against Slack, Jira, Confluence, CRMs, and internal APIs.
5. Pivot & Prove Impact
Chaining a leaked key, a write-capable tool, or an SSRF into demonstrable business harm—within scope. An attacker only has to be right once, so we don't just report the flaw; we show the damage it enables.
6. Report, Validate & Re-Test
Findings framed as organizational risk and mapped to the frameworks your board recognizes—with honest, attempt-count caveats for non-deterministic attacks. After you remediate, we re-test to confirm the fix holds under real adversarial conditions.
Let's identify prompt injection, model extraction, and adversarial attacks in your AI systems before threat actors do.
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