Your AI agent handles real money and real data. We attack it before someone else does.
Prevent unauthorized fund movements through agent exploitation
Protect user data from LLM extraction attacks
Meet emerging AI security compliance requirements
Ship AI features with confidence
Autonomous AI agents with tool use
Customer-facing AI systems
APIs, frontends & outer interfaces (OWASP)
of deployed LLM applications are vulnerable to prompt injection attacks
in losses attributed to AI-related security failures across Web3 and fintech
AI agents can be manipulated into unauthorized fund transfers and data exfiltration
EU AI Act and NIST AI RMF demand documented security testing
A realistic example of the vulnerabilities our team uncovers
A DeFi protocol integrated an LLM-powered trading assistant to help users manage positions. During our red team engagement, we discovered a prompt injection vector that could trick the agent into approving unauthorized withdrawals. The attack worked by crafting a malicious input that overrode the agent's safety guardrails and issued a tool call to the withdrawal function.
Systematic adversarial testing tailored to AI agent architectures
We analyze your AI agent's architecture, tool integrations, and decision-making pipeline to understand its attack surface.
Systematic testing of prompt injection vectors including direct injection, indirect injection through external data, and multi-turn manipulation.
Testing the agent's tool-calling capabilities for unauthorized actions, privilege escalation, and unintended side effects.
Evaluating whether the agent can be manipulated to leak sensitive data, internal prompts, training data, or user information.
Testing the robustness of content filters, safety mechanisms, and output guardrails against adversarial techniques.
Comprehensive documentation of findings with actionable recommendations to harden your AI agent against real-world threats.
Comprehensive adversarial testing across all AI threat vectors
Testing resistance to direct and indirect prompt injection attacks that attempt to override system instructions.
Evaluating whether agents can be tricked into executing unauthorized actions through their tool integrations.
Assessing the agent's resilience against attempts to extract sensitive information or manipulate its knowledge.
Testing the effectiveness of safety guardrails and alignment measures against adversarial manipulation.
Industry-standard frameworks for AI security assessment
Following the OWASP Top 10 for Large Language Model Applications to systematically assess AI-specific vulnerabilities.
Leveraging the MITRE ATLAS framework for adversarial threat modeling of AI and machine learning systems.
Leveraging Google's Secure AI Framework (SAIF), a practitioner's guide to navigating AI security, addressing 15 inherent risks in AI development with emphasis on securing autonomous AI agents.
Comprehensive documentation and actionable hardening recommendations
High-level overview for stakeholders
Detailed attack scenarios and results
Prioritized risk severity matrix
Guardrail & prompt hardening steps
Our red teamers hold industry-recognized certifications
Hack The Box advanced certification covering web application exploitation, demonstrating expert-level offensive security skills.
View Certification →Developed with Google, covering prompt injection, model privacy, adversarial techniques, and AI-specific red teaming aligned with Google SAIF.
View Path →Altered Security certification focused on Active Directory exploitation, lateral movement, and enterprise red teaming techniques.
View Certification →Get a free 30-minute security assessment. We will review your codebase scope and flag the top 3 risk areas.
No commitment required. Typical audits start within 1–2 weeks.