Adversarial security testing for AI agents, LLM-powered applications, and autonomous systems
Autonomous AI agents with tool use
Customer-facing AI systems
APIs, frontends & outer interfaces (OWASP)
AI agents introduce a new class of security risks that traditional testing cannot catch
AI agents can be manipulated through crafted inputs to bypass safety measures and execute unintended actions
Agents with access to sensitive data can be tricked into exposing confidential information through adversarial queries
Agents with tool access can be exploited to perform unauthorized actions with real-world consequences
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 →Let's discuss your project and ensure your security!