AI & LLM Security TestingAI Systems Security Assessment
Enterprise-grade penetration testing specifically designed for AI agents, LLM deployments, and autonomous systems. Identify critical vulnerabilities in your AI infrastructure before threat actors do.
Penetration Testing for the AI Era
As organizations deploy AI agents and LLM-powered systems, new attack vectors emerge. Our specialized penetration testing methodology targets AI-specific vulnerabilities, from autonomous agent manipulation to multi-modal model exploitation. We help security leaders understand and mitigate risks in GPT, Claude, Gemini, and custom AI deployments.
AI System Attack Vectors We Test
AI Agent Manipulation
Attacking autonomous AI agents and multi-agent systems
- Agent goal hijacking and redirection
- LLM jailbreaking and prompt injection
- Multi-agent consensus manipulation
- System prompt extraction
- Memory corruption and false memories
- Agent communication interception
RAG & Knowledge Base Attacks
Exploiting retrieval-augmented generation systems
- Knowledge base poisoning
- Retrieval manipulation attacks
- Vector database injection
- Embedding space manipulation
- Context window overflow
- Similarity search hijacking
Tool & Function Calling Exploits
Compromising AI tool use and function calling
- Function calling injection
- Tool permission escalation
- API key extraction
- Code execution manipulation
- External service hijacking
- Tool chain exploitation
AI Security Testing Methodology
Comprehensive testing approach specifically designed for AI systems and autonomous agents
AI System Reconnaissance
Map the AI attack surface
Key Activities
- Model fingerprinting and version detection
- Agent capability enumeration
- Tool and API discovery
- Prompt template extraction
- Guard rail and filter identification
- Integration point mapping
Attack Vector Development
Craft AI-specific attack payloads
Key Activities
- Adversarial prompt engineering
- Jailbreak technique development
- Encoding and obfuscation strategies
- Multi-turn attack sequences
- Cross-modal attack vectors
- Indirect injection payload creation
Exploitation Campaign
Execute systematic penetration tests
Key Activities
- Direct model attacks
- Agent behavior manipulation
- Memory and context corruption
- Tool abuse and escalation
- Data extraction attempts
- System-to-system propagation
Attack Persistence
Establishing persistent access and expanding control
Key Activities
- Persistent prompt injection
- Agent memory implants
- Knowledge base backdoors
- Cross-agent contamination
Why AI Penetration Testing is Critical
Prevent Agent Hijacking
Identify vulnerabilities that could allow attackers to take control of your AI agents and autonomous systems.
Protect Sensitive Data
Discover data leakage paths through LLMs, including training data extraction and prompt injection attacks.
Stop Jailbreak Attacks
Test and strengthen guardrails against sophisticated jailbreaking techniques targeting your AI deployments.
Prevent Financial Damage
Identify token abuse, resource exhaustion, and cost amplification vulnerabilities in your AI infrastructure.
Comprehensive Penetration Test Results
Comprehensive Security Reports
Executive and detailed technical reports with findings and evidence
AI Security Framework
Hardening guidelines for AI systems and LLM deployments
Attack Chain Documentation
Detailed attack vectors and exploitation techniques