AI Penetration Testing
Security testing for AI-enabled applications and LLM integrations
AI-enabled products introduce an entirely new class of vulnerabilities — prompt injection, indirect prompt abuse, data leakage through model outputs, broken access controls around AI features, and supply chain risks from external model APIs. NeedSec tests AI applications against the OWASP LLM Top 10 and real-world adversarial techniques to find what automated tools miss.
Practical assessment
Testing and review work is hands-on and tailored to your environment - not a generic checklist.
Clear, evidence-led output
Every finding includes evidence, business context, and a concrete path to resolution.
Compliance-aware approach
Work is structured around real security improvement - and mapped to relevant frameworks where needed.
What We Assess
Practical testing aligned to business risk
NeedSec combines manual testing, technical validation, and clear reporting so your team understands what matters and how to fix it.
Prompt injection testing — direct and indirect injection through user input and retrieved content
Insecure output handling — XSS, code execution, and downstream system abuse via model responses
RAG pipeline security — data source poisoning, retrieval manipulation, and output extraction
LLM agent framework abuse — tool call hijacking, excessive agency, and chained prompt exploitation
Sensitive data exposure — PII, secrets, and confidential content leaking through model outputs
Authentication and authorisation around AI features — context isolation and session separation
Plugin and tool integration risks — third-party LLM tools, function calling, and API exposure
Model denial of service — resource exhaustion through crafted prompts and token flooding
Supply chain and model provider risks — dependency integrity and API key exposure
Business logic abuse via AI assistants — bypassing guardrails and intended workflow limits
Fine-tuned and embedded model security — training data extraction and model inversion risk
Audit logging gaps — incomplete observability over AI interactions and model decision trails
What You Get
Clear deliverables for security, compliance, and remediation
Every engagement concludes with a structured deliverable package so your team can act on findings without guesswork.
AI security risk summary
An overview of AI-specific risks identified, from prompt injection to data exposure.
OWASP LLM Top 10 findings report
Findings mapped directly to the OWASP Top 10 for Large Language Model applications.
Prompt injection evidence and examples
Real examples of successful prompt injection attempts, with reproduction steps.
Data exposure technical findings
Technical detail on how sensitive data could be extracted through model outputs or APIs.
Remediation guidance for AI stacks
Fix guidance tailored to LLM integrations, RAG pipelines, and agent frameworks.
Integration and plugin risk notes
Risk assessment of third-party tools, plugins, and API integrations connected to the AI system.
Severity-rated vulnerability list
Every finding ranked by CVSS severity so remediation effort is prioritised correctly.
Retest validation
Confirmation that previously identified vulnerabilities have been fixed and no longer present after remediation.
Need help scoping this service?
Tell NeedSec about your environment, compliance goal, or security concern. We will help define the right assessment approach.