OWASP Top 10 LLM Vulnerabilities Testing

Common LLM vulnerabilities include direct and indirect prompt injection, jailbreaks, system prompt leakage, sensitive information disclosure, insecure output handling, excessive agent permissions, insecure plug-ins or tool use, and model theft. RAG-enabled applications can also expose confidential documents through weak access controls, cross-tenant retrieval, poisoned content, or manipulated queries. Testing should assess the model and its connected application components together.

In cybersecurity, LLM means large language model: an AI model that processes and generates human-like text. Security teams use the term to describe both the model and the application built around it, including prompts, retrieval systems, agents, APIs, tools, and data stores. LLM security focuses on whether attackers can manipulate model behavior, access sensitive data, or trigger unsafe actions through those connected components.

An LLM vulnerability is a weakness in a language-model system that an attacker can exploit to bypass instructions, reveal data, alter outputs, misuse connected tools, or extract proprietary information. The weakness may exist in the model interaction, prompt design, RAG retrieval logic, agent permissions, output handling, or integrations. OWASP’s LLM Top 10 provides a structured way to identify and manage these risks.

OWASP LLM Top 10 testing evaluates the major risk categories affecting LLM applications, including prompt injection, sensitive information disclosure, supply-chain weaknesses, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and model theft. Vynox tests these categories through realistic adversarial scenarios across your model, retrieval pipeline, agents, integrations, and application controls.

A standard AI and LLM penetration testing engagement from Vynox typically takes 5–7 business days. Focused RAG pipeline, agent security, or model inversion assessments usually take 10–15 business days, while a comprehensive AI red-team engagement generally takes 3–5 weeks. Timeline depends on the number of applications, model integrations, input channels, tool permissions, and environments included in scope.

Yes. Vynox can test a running LLM application externally, as an attacker would, without source-code access. This approach validates exploitable behavior across prompts, documents, APIs, agents, and connected tools. Source-code review can be added when available to examine prompt construction, retrieval authorization, tool definitions, input validation, and output handling that may not be observable from the application interface alone.

Vynox manually applies more than 40 prompt injection and jailbreak techniques, including direct instruction overrides, indirect injection through documents and tool outputs, role-play exploits, token manipulation, encoding bypasses, and multi-turn attack chains. Testing determines whether attackers can extract system prompts, defeat safeguards, access restricted data, or cause unintended tool actions. Findings include proof, impact, and reproducible remediation guidance.

Yes. Vynox maps relevant AI security findings to SOC 2 and ISO 27001 control evidence requirements, helping teams use a single testing engagement for remediation and audit preparation. Reports include an executive summary, technical findings, evidence screenshots, CVSS scores, and developer-ready fixes. For high-risk AI systems, AI red teaming can also support EU AI Act Article 15 adversarial-testing requirements.