Why a Traditional Pentest Doesn't Cover Your AI
A standard penetration test validates your network, web app, and infrastructure. It says nothing about whether your LLM assistant can be talked into leaking another customer's data, whether a poisoned document in your knowledge base can hijack a response, or whether your AI agent can be coerced into generating and running malicious code.
AI red teaming closes that gap. We treat your AI features as a live attack surface and probe them the way a motivated adversary would—chaining prompt injection, tool abuse, and data-exfiltration paths into demonstrable business impact. Then we hand your team findings mapped to recognized frameworks, with concrete remediation, not vague warnings about "AI risk."
The Attack Surface We Test
Engagements are scoped against the OWASP Top 10 for LLM Applications (2025). We focus on the risks that produce real impact for your deployment—not a checkbox sweep.
Prompt Injection (LLM01)
Direct and indirect injection, jailbreaks, and guardrail bypass—including payloads hidden in documents, web pages, and tool output that the model later processes.
Sensitive Information Disclosure (LLM02)
Extraction of internal documents, other users' data, secrets, and PII through the model and its connected tools.
Improper Output Handling (LLM05)
Model output that reaches downstream systems unsanitized—driving XSS, SSRF, SQL injection, or remote code execution, and malicious-code generation that pivots to user endpoints.
Excessive Agency (LLM06)
Over-permissioned agents and tools that can be coerced into actions far beyond their intended scope—sending email, moving money, touching production.
System Prompt Leakage (LLM07)
Recovery of system prompts, hidden instructions, and the guardrail logic an attacker needs to plan the next move.
Vector & Embedding Weaknesses (LLM08)
RAG-specific attacks: embedding poisoning, retrieval manipulation, and cross-tenant leakage in your vector store.
Misinformation (LLM09)
Hallucination, fabricated facts, and unsafe overreliance—surfaced with adversarial prompting and automated tooling such as garak to find where the model confidently gets it wrong.
Unbounded Consumption (LLM10)
Resource-exhaustion and denial-of-wallet attacks—crafted inputs that can degrade or take down a self-hosted model, or run up uncapped inference cost on a metered one.
Manual, adversarial, and threat-informed—aligned to MITRE ATLAS, the adversarial-AI counterpart to MITRE ATT&CK. Automated scanners catch the obvious; real impact comes from chaining.
1. Recon & Threat Model
Map the model, its tools, data sources, agents, and trust boundaries. Identify what an attacker would actually want.
2. Probe
Manual prompt injection, jailbreaks, tool abuse, and RAG manipulation against the live system—not a synthetic benchmark.
3. Chain & Exploit
Combine individual weaknesses into a full kill chain that demonstrates concrete business impact—data theft, endpoint compromise, account abuse.
4. Report & Retest
Findings mapped to OWASP LLM and ATLAS, with prioritized remediation—plus a retest to confirm the fixes hold.
Every engagement produces deliverables your team can act on immediately.
Executive Summary
A plain-language account of what we found, what it means for the business, and what to prioritize—written for leadership, not just engineers.
Technical Findings
Each finding with reproduction steps, evidence, severity, and its OWASP LLM / MITRE ATLAS mapping—so your team can verify and fix with confidence.
Remediation & Retest
Specific, prioritized fixes—input/output controls, agent sandboxing, allow-listing, detection rules—followed by a retest to confirm closure.
If any of these describe you, your AI features are likely in scope for a real attacker before they're in scope for your security program:
- You're launching an LLM-powered feature—chatbot, copilot, agent, or RAG search—and need a security sign-off before release.
- A customer security questionnaire now asks how your AI is tested, and you don't have an answer.
- An auditor flagged your LLM agent as out of scope for your existing pentest.
- An internal AI-governance or responsible-AI mandate requires adversarial testing.
- Your agents can take real actions—sending email, executing code, touching production or customer data.
- Leadership is asking, plainly, "is our AI safe to ship?"
AI red teaming is offensive security applied to a new attack surface. The reason ours is credible is that we were breaking real systems—networks, web apps, ICS—long before LLMs existed. See the offensive security foundation behind it.
