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AI & LLM Security Testing

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.

LLM jailbreaking and prompt injection
RAG poisoning and knowledge base attacks
Agent goal hijacking and manipulation
Tool-use privilege escalation
Multi-agent coordination attacks
Vector database exploitation

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