Adversarial Evaluation

Exposing vulnerabilities before your adversaries do.

Generative AI introduces a profoundly new attack surface. Our specialized ML security researchers execute rigorous, adversarial red teaming to uncover prompt injections, multi-turn jailbreaks, and sensitive data extraction vectors in your production models.

Schedule Red Team Engagement

Attack Vectors Evaluated

Comprehensive Threat Simulation

We do not rely solely on automated vulnerability scanners. Our human-led red teaming simulates advanced persistent threats against complex language model architectures.

Prompt Injection

Testing the model's resilience against indirect and direct prompt injections that attempt to hijack the system's operational instructions or manipulate subsequent outputs.

Multi-Turn Jailbreaks

Simulating long-context conversational attacks where benign initial prompts gradually shift context to bypass constitutional safety filters.

Data Extraction

Evaluating the likelihood of the model regurgitating proprietary training data, PII, or internal systemic prompts through targeted adversarial querying.

Red Team Methodology

Structured Adversarial Campaigns

Our process combines high-velocity automated fuzzing with deeply creative, manual exploitation by ML security specialists.

Threat Architecture Mapping

Analyzing the integration points, memory stores, and API access layers of your generative system to define the attack surface.

II.

Automated Fuzzing

Subjecting the model to millions of known jailbreak variants, toxic prompts, and encoding bypasses to establish a baseline security posture.

III.

Manual Exploitation

Senior red teamers craft bespoke, context-aware attacks designed to exploit specific business logic and bypass automated guardrails.

IV.

Remediation Blueprint

Delivery of a comprehensive threat matrix outlining vulnerability severity and actionable architectural mitigation strategies.

Frequently Asked Questions

AI Red Teaming is a structured adversarial evaluation where security researchers intentionally try to bypass an AI system's safety filters, using tactics like prompt injections and jailbreaks to expose vulnerabilities before deployment.

Traditional pentesting focuses on network and software vulnerabilities (e.g., SQL injection, XSS). AI Red Teaming specifically targets the statistical and behavioral nature of Large Language Models, focusing on semantic exploits, adversarial prompts, and model extraction techniques that cannot be detected by standard security scanners.

No. We typically evaluate against a staging or pre-production environment. Our testing runs parallel to your development cycle, allowing your team to continue building while we uncover and report vulnerabilities.