AI Red Team Operations
Our operators simulate real-world attacks against your AI models, LLM integrations, and machine learning pipelines—finding prompt injection, training data poisoning, model extraction, and adversarial attacks before threat actors do. Every finding is mapped to OWASP LLM Top 10, MITRE ATLAS, and the AI frameworks your auditors recognize.
Request an AI Red Team Assessment →AI red teaming goes beyond traditional penetration testing—we attack the model itself, the training pipeline, the inference layer, and every integration point where your AI meets production systems.
LLM & Model Security
Direct testing of your language models and AI systems—prompt injection, jailbreaking, role hijacking, system prompt extraction, and adversarial inputs designed to bypass guardrails and manipulate model behavior. We test GPT integrations, custom-trained models, and third-party AI APIs.
Training Data Poisoning
Adversarial manipulation of training pipelines to introduce backdoors, trigger unexpected model behavior, or degrade performance on targeted inputs. We simulate supply chain attacks on training data, model weight tampering, and poisoned datasets.
Model Extraction & Inversion
Attempting to steal model architecture, weights, or training data through carefully crafted inference queries. We test for membership inference attacks, model inversion, and proprietary business logic exposure that could compromise your intellectual property.
AI System Integration Testing
Downstream exploitation of unvalidated LLM outputs leading to XSS, CSRF, SSRF, code execution, and privilege escalation in systems that trust the model response without proper sanitization. We test every integration point where AI meets your application stack.
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.
We follow a structured approach mapped to OWASP LLM Top 10, MITRE ATLAS, and NIST AI Risk Management Framework—ensuring findings tie directly to the standards your auditors and leadership recognize.
1. Reconnaissance & Model Profiling
Identifying your AI attack surface—model endpoints, APIs, integrations, training pipelines, and data flows. We map the full lifecycle from training to inference to understand exactly where adversarial attacks can be introduced.
2. Adversarial Testing & Exploitation
Systematic simulation of AI-specific attacks: prompt injection, jailbreaking, model extraction, data poisoning, and integration exploits. Every test is operator-led—no automated scanners, no generic scripts.
3. Framework Mapping & Documentation
Every finding is mapped to OWASP LLM Top 10, MITRE ATLAS tactics, and relevant compliance frameworks. You receive evidence-backed reports with remediation guidance tied to the standards your board understands.
4. Remediation Validation & Re-Testing
After you patch, we re-test to confirm the fix holds under adversarial conditions. We don't just document vulnerabilities—we validate that your guardrails actually work against real attack patterns.
Let's identify prompt injection, model extraction, and adversarial attacks in your AI systems before threat actors do.
Request an AI Red Team Assessment →