AI Red Teaming
Find Vulnerabilities Before Attackers Do
Traditional security testing wasn't designed for probabilistic systems. We use multi-agent attack simulations to probe your AI systems for vulnerabilities that conventional tools miss.
Why AI Systems Need Different Testing
Standard penetration testing assumes predictable behavior. Query a database, exploit a buffer overflow, escalate privileges. The attack surface is static and the responses are deterministic.
AI systems don't work that way. Large language models generate different outputs for identical inputs. Agentic workflows chain multiple AI components with emergent behaviors. The attack surface shifts with every interaction.
Three fundamental mismatches exist between traditional security testing and AI systems: static tests can't assess dynamic behavior, single-point probes miss contextual attacks that build influence across conversations, and deterministic methods can't evaluate emergent properties that weren't explicitly programmed.
Our Testing Methodology
Threat Modeling
Map your AI systems' attack surfaces. Which components have external access? What data can they reach? Where do agents have autonomous authority? We document exposure points before testing begins.
Baseline Establishment
Define expected and forbidden behaviors. What should your AI systems refuse to do? What information should they never reveal? These baselines become success criteria for adversarial testing.
Multi-Agent Attack Simulation
Coordinated attacker, defender, and evaluator agents probe your systems systematically. This approach more accurately simulates how real attackers operate: iterating on strategies, learning from failures, and exploiting successful techniques.
Agentic-Specific Testing
For autonomous AI systems, we test attack vectors unique to agentic workflows: memory poisoning, tool exploitation, goal hijacking, and cascading failures across multi-agent architectures.
Continuous Integration
AI red teaming shouldn't be a one-time assessment. As models are updated and prompts are modified, new vulnerabilities emerge. We help integrate testing into your deployment pipelines.
Remediation Guidance
Every finding comes with actionable recommendations. We don't just identify vulnerabilities—we help you understand root causes and implement effective defenses that maintain model performance.
Vulnerability Categories We Test
Prompt Injection and Jailbreaks
Malicious inputs that manipulate LLM behavior, bypass content filters, or override system instructions. We test both direct injection and indirect attacks through retrieved content.
Data Extraction and Leakage
Techniques that cause models to reveal training data, system prompts, or sensitive information. We probe for memorization vulnerabilities and context window exploitation.
Agent Goal Hijacking
Attacks that manipulate an agent's objectives through prompt injection or context poisoning, causing it to pursue malicious goals while appearing to function normally. (OWASP ASI01)
Tool Misuse and Exploitation
AI agents with access to external tools can be tricked into using those tools maliciously. An agent with code execution capabilities becomes a privilege escalation vector. (OWASP ASI02)
Memory and Context Poisoning
Agents with persistent memory can have that memory corrupted. Injecting malicious context creates persistent backdoors affecting all future interactions. (OWASP ASI06)
Cascading Agent Failures
Multi-agent systems create dependency chains. When one agent fails or is compromised, the failure propagates through connected agents, potentially amplifying impact exponentially. (OWASP ASI08)
Framework Alignment
Our testing methodology aligns with leading AI security frameworks, ensuring comprehensive coverage of known vulnerability patterns and emerging threats.
OWASP Top 10 for LLM Applications
Comprehensive testing against the most critical LLM security risks: prompt injection, insecure output handling, training data poisoning, and more.
OWASP Top 10 for Agentic Applications
Specialized coverage for autonomous AI systems including goal hijacking, tool misuse, privilege abuse, and rogue agent behaviors.
Read our analysis →NIST AI Risk Management Framework
Systematic approach to identifying, assessing, and managing AI-related risks aligned with federal guidelines and industry best practices.
MITRE ATLAS
Testing against documented real-world attack techniques from the Adversarial Threat Landscape for AI Systems knowledge base.
The Sentinel Nexus Approach
AI red teaming doesn't exist in isolation. It's part of an integrated security approach that spans implementation, testing, and governance. We connect adversarial findings to remediation strategies and compliance requirements.
Our red teaming work integrates with our other security services to provide comprehensive protection for your AI investments.
Security Built Into Pipelines
Red teaming findings inform secure development practices. We help you build defenses into your ML pipelines from the architecture phase.
Learn about Secure AI Development →Compliance and Documentation
Red team assessments generate documentation required for regulatory compliance and audit trails under frameworks like EU AI Act and NIST.
Learn about AI Governance →Ready to test your AI systems?
Let's discuss how adversarial testing can strengthen your AI security posture.
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