AI Chatbot Penetration Testing

AI Chatbot Security Testing

Traditional penetration testing methodologies were not designed for AI systems. LLM-based chatbots have unique attack surfaces — natural language interfaces, RAG retrieval pipelines, tool integrations, and context window management — that require specialized testing techniques.

What Makes AI Chatbots Different to Test

Unlike traditional web applications, AI chatbots process natural language and can be manipulated through the very interface they were designed to use. A chatbot that passes all conventional security checks can still be vulnerable to prompt injection, jailbreaking, and RAG poisoning attacks.

Prompt Injection (OWASP LLM01): Attackers embed instructions in user input or retrieved content to override your chatbot’s intended behavior.

Jailbreaking: Technique-based attacks bypass safety guardrails to make your chatbot produce policy-violating or harmful outputs.

RAG Poisoning: Malicious content injected into your knowledge base causes your chatbot to retrieve and act on attacker-controlled data.

Data Exfiltration: Crafted prompts extract PII, credentials, API keys, or business intelligence from your chatbot’s accessible data.

Our Testing Methodology

Every engagement follows a structured, OWASP LLM Top 10-aligned methodology. We map every finding to a recognized vulnerability category so your team can prioritize remediation with confidence.

Phase 1 — Reconnaissance & Attack Surface Mapping: We document all input vectors, system prompt structures, RAG pipelines, tool integrations, and API endpoints.

Phase 2 — Active Attack Simulation: We execute the full OWASP LLM Top 10 attack catalog including prompt injection, jailbreaking, context manipulation, token smuggling, and indirect injection.

Phase 3 — Data Exfiltration Testing: We attempt to extract system prompt contents, PII from connected data sources, API credentials, and business-sensitive information.

Phase 4 — API & Infrastructure Testing: We test authentication, rate limiting, authorization boundaries, and API endpoint abuse scenarios.

Phase 5 — Reporting & Remediation Guidance: Detailed report with findings, proof-of-concept payloads, severity ratings, and prioritized remediation steps.

What We Test

Our assessments cover every major attack surface specific to LLM-based AI chatbots.

Prompt Injection. Direct and indirect injection attacks including role-play manipulation, multi-turn sequences, and environment-based injection through retrieved content.

Jailbreaking. Safety guardrail bypass techniques including DAN variants, persona attacks, token smuggling, and multi-step manipulation sequences.

RAG Poisoning. Knowledge base contamination attacks that cause your chatbot to retrieve and act on malicious, attacker-controlled content from your own data sources.

System Prompt Extraction. Techniques to reveal confidential system prompt contents, business rules, safety instructions, and configuration secrets that should remain private.

Data Exfiltration. Attacks that extract PII, API credentials, internal business data, and sensitive documents from the chatbot’s connected data sources and context.

API & Auth Abuse. Rate limit bypass, authentication weakness exploitation, authorization boundary testing, and denial-of-service scenarios against LLM API endpoints.

What You Receive

Every engagement delivers a structured, actionable security report — written for both executives and engineering teams.

Executive Summary: Non-technical overview of findings, risk posture, and remediation priorities for leadership.

Attack Surface Map: Full diagram of your chatbot’s components, data flows, and identified entry points.

Findings Register: All vulnerabilities with severity (Critical / High / Medium / Low / Informational), CVSS-equivalent score, and OWASP LLM Top 10 mapping.

Proof-of-Concept Demonstrations: Reproducible attack payloads for every confirmed finding, so your team can verify and understand the vulnerability.

Remediation Guidance: Specific, prioritized fixes with effort estimates — including code-level recommendations where applicable.

Re-test Report: Follow-up assessment within 30 days confirming which findings have been successfully remediated.

What is AI chatbot penetration testing?

AI chatbot penetration testing is a structured security assessment that simulates real-world attacks against your AI chatbot system. Our security engineers test for prompt injection, jailbreaking, data exfiltration, RAG poisoning, context manipulation, and API abuse — the same vulnerabilities catalogued in the OWASP LLM Top 10.

Do you test chatbots built on other platforms?

Yes. We test AI chatbots built on any platform — GPT-based, Claude-based, Gemini-based, or open-source LLMs — whether deployed via API, embedded widget, or custom infrastructure. Our methodology is model-agnostic.

What is the OWASP LLM Top 10?

The OWASP LLM Top 10 is the industry-standard list of the most critical security risks for applications built on large language models. It covers prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, and more. Our testing methodology maps directly to all 10 categories.