About AI & LLM Penetration Testing
AI and LLM penetration testing to identify and validate real risks across LLM apps, RAG pipelines, and AI integrations.
AI / LLM
AI & LLM Penetration Testing
AI and Large Language Model (LLM) Vulnerability Assessment and Penetration Testing (VAPT) to identify and validate real security risks across LLM applications, RAG pipelines, and AI integrations.
PROMPTS // RAG // TOOL USE // GUARDRAILS // PROMPTS // RAG // TOOL USE // GUARDRAILS
Service Overview
AI & LLM Penetration Testing focuses on identifying weaknesses in how AI systems process inputs, manage context, and interact with external tools, data sources, and backend systems.
The assessment evaluates how prompts can be manipulated, how context can be influenced, and how integrations can be abused to alter system behavior, expose sensitive data, or trigger unintended actions. This includes testing how AI systems handle instructions, memory, retrieval mechanisms, and downstream execution paths.
The objective is to determine how AI-driven systems can be manipulated in practice, what data can be accessed or leaked, and how workflows can be influenced beyond intended controls. Findings are validated to ensure they represent real and actionable risk.
Attack Path Validation
From prompt manipulation to system-level impact
Weaknesses are assessed across inputs, context, memory, and integrations, focusing on how issues such as prompt injection, context poisoning, or tool misuse can be combined to extract data, bypass controls, or trigger unintended actions across the system.
Benefits
Clear visibility into AI-specific risk
Identifies how AI behavior can be manipulated through inputs, context, and integrations.
Focus on high-impact abuse scenarios
Highlights weaknesses that lead to data exposure, control bypass, or unintended execution.
Confirmed impact across AI workflows
Shows how vulnerabilities affect responses, memory, retrieval, and backend actions.
Accurate understanding of system behavior
Reflects how AI systems behave under adversarial interaction conditions.
Why Choose VulnXperts
What We Test
A structured review of how AI systems process inputs, manage context, and interact with integrations to identify conditions that lead to unintended behavior, data exposure, or unsafe execution.
How we approach testing
Testing begins with understanding how the AI system is structured, then focuses on manipulating inputs, context, and integrations to identify where controls fail under adversarial conditions.
AI interaction flow and inference pipeline analysis (input handling, prompt construction, response generation)
Identification of exposed interaction points (chat interfaces, APIs, agents, plugins, webhooks)
Prompt injection and jailbreak techniques (instruction override, encoding bypass, role confusion)
Multi-turn interaction abuse (context drift, instruction persistence, role escalation)
Context and memory manipulation (context poisoning, memory injection, state contamination)
System prompt and hidden instruction leakage
Retrieval-Augmented Generation (RAG) security (retrieval abuse, ranking manipulation, context injection)
RAG poisoning via malicious data ingestion
Vector database and embedding behavior (similarity abuse, cross-context leakage)
Cross-session and cross-user data leakage
Session handling and memory isolation
Authorization and access control via AI responses
Agent and function-calling behavior (tool misuse, chained execution abuse)
Backend integration abuse (API invocation, indirect command execution, trust boundary issues)
Business logic manipulation through AI workflows
Input validation (malformed prompts, encoding layers, structured payloads)
Output validation (over-disclosure, unsafe responses, leakage in outputs)
Sensitive data exposure (responses, memory, logs, embeddings, integrations)
Indirect prompt injection via external content
Rate limiting and abuse scenarios (token usage, cost amplification)
Safety and guardrail bypass techniques
Runtime behavior (environment exposure, secret leakage, execution boundary issues)
FAQs
Ready to scope this engagement?
Tell us what needs to be tested. We will define scope, coverage, and approach based on your AI architecture.