LLM & GenAI Security

LLM/GenAI Penetration Testing

Elevate Your Security with DSecured's Expert LLM/GenAI Penetration Testing: Renowned Ethical Hackers Deliver Clear, Actionable Reports Free of False Positives.

Our seasoned ethical hackers employ advanced techniques, including live hacking events, to uncover vulnerabilities in your LLM/GenAI systems. Each comprehensive report not only highlights critical weaknesses but also provides actionable steps for your IT team to swiftly mitigate risks. Trust DSecured for unparalleled precision and unmatched quality in offensive security services.

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Prompt injection & data exfiltration under control

GenAI projects often fail due to missing protection mechanisms against creative prompt attacks. We specifically test your LLM setup for jailbreaks, data leaks and security gaps in the toolchain before your users do.

What is an LLM Penetration Test?

LLM penetration tests are specialized security assessments for applications with Large Language Models and GenAI - from ChatGPT-like chatbots to RAG systems (Retrieval-Augmented Generation) and autonomous AI agents. We test for OWASP LLM Top 10 vulnerabilities: Prompt Injection (jailbreaking, system prompt leaks), Insecure Output Handling (XSS/SQLi via LLM output), Training Data Poisoning, Model Denial of Service, Supply Chain Vulnerabilities (OpenAI/Anthropic/Hugging Face), Sensitive Information Disclosure (PII leakage from training data), Insecure Plugin Design (LangChain/function calling), Excessive Agency (autonomous agents without guardrails), Overreliance (hallucinations as security risk), and Model Theft via extraction attacks.

Prompt Injection & Jailbreaking: System Prompt Bypass Manipulation of the LLM via crafted prompts: DAN attacks (Do Anything Now), token smuggling, multi-turn attacks, payload splitting across multiple messages. Goal: Bypassing safety guardrails, extracting system prompts, generating toxic/harmful content.

RAG Poisoning & Vector DB Attacks: Knowledge Base Manipulation Injection of malicious documents into vector databases (Pinecone, Weaviate, ChromaDB), cross-context information leakage, privilege escalation via RAG context, embedding manipulation.

Agent Hijacking & Tool Abuse: Compromising Autonomous AI Systems LangChain/AutoGPT/BabyAGI security: Function calling exploits, tool injection (shell commands via LLM), agent loop DoS, sandbox escapes, API key leakage via agent logs.

We deliver prioritized results with reproducible PoC prompts, concrete mitigation strategies (input sanitization, output validation, guardrail implementation via LlamaGuard/NeMo Guardrails), and - if desired - management summaries for stakeholders, compliance audits (AI Act, GDPR), and legal teams.

Should an LLM Pentest be Combined with a Web Pentest?

Yes, in most cases a combined approach makes sense. LLM applications are web apps with AI backends - classic vulnerabilities like SQL injection, XSS, CSRF interact with LLM vulnerabilities: A SQL injection can compromise training data, XSS in LLM output can lead to stored XSS, SSRF via LLM function calling enables cloud metadata extraction. Recommendation: Combination of classic web app pentest + LLM-specific red teaming for complete coverage.

Typical LLM Applications in Pentests

In LLM penetration tests, we encounter various GenAI systems:

ChatGPT-like Chatbots RAG Systems (Retrieval-Augmented Generation) Code Completion Tools (GitHub Copilot Alternatives) Customer Support Bots Document Q&A Systems Autonomous Agents (LangChain, AutoGPT) Fine-Tuned Models (Domain-Specific LLMs) Multi-Modal Systems (GPT-4V, DALL-E Integration) Voice Assistants with LLM Backend Content Moderation Systems AI-Powered Search (Perplexity Clones) Email/Text Generation Tools

OWASP LLM Top 10: The Most Critical Vulnerabilities

The OWASP LLM Top 10 is the industry standard for LLM security - we systematically test all ten categories and deliver prioritized results with concrete mitigations.

01

Prompt Injection

Manipulation of the LLM via crafted prompts: Direct injection (user input), indirect injection (via websites/emails/documents), jailbreaking, system prompt extraction, DAN attacks, token smuggling. Risk: Bypassing guardrails, generating harmful outputs.

02

Insecure Output Handling

LLM output is passed unfiltered to backend systems: XSS via LLM response, SQL injection through generated queries, command injection via shell calls, SSRF through URL generation. Risk: Classic web vulnerabilities via LLM output.

03

Training Data Poisoning

Manipulation of training data or fine-tuning datasets: Backdoor injection, bias amplification, PII leakage via training data, toxicity injection. Risk: Long-term compromise of the model.

04

Model Denial of Service

Resource exhaustion via long prompts, infinite loops, complex queries, multi-turn DoS, token bombing. Risk: Service unavailability, extremely high API costs (cost attack against OpenAI/Anthropic).

05

Supply Chain Vulnerabilities

Third-party model risks (Hugging Face, OpenAI, Anthropic), compromised plugins, malicious pre-trained models, outdated libraries (LangChain, LlamaIndex), API key leakage via dependencies. Risk: Complete compromise via supply chain.

06

Sensitive Information Disclosure

PII leakage from training data, memorization attacks (model reveals training data), system prompt extraction, API key leaks via chat history, cross-user information leakage in multi-tenant systems. Risk: GDPR violations, data protection breaches.

07

Insecure Plugin Design

LangChain/function calling vulnerabilities: Unrestricted tool access, missing input validation, plugin injection, excessive permissions, insecure authentication for plugins. Risk: RCE via tool abuse, data exfiltration.

08

Excessive Agency

Autonomous agents with too many permissions: Unrestricted code execution, uncontrolled database access, file system manipulation without approval, unbounded API calls, missing human-in-the-loop. Risk: Complete system compromise via agent.

09

Overreliance

Trust in erroneous LLM outputs: Hallucinations as security risk, faulty code generation (security bugs via Copilot), misinformation in critical decisions, missing verification of LLM responses. Risk: Business logic flaws, compliance issues.

10

Model Theft

Extraction attacks: Model stealing via query-based attacks, fine-tuning data extraction, membership inference attacks, watermark removal, model inversion. Risk: IP theft, competitors gain access to proprietary models.

Which Vulnerabilities & Attack Scenarios Do We Test in LLM Pentests?

We simulate realistic attacks on your LLM application - from prompt injection chains to RAG poisoning and agent hijacking with complete system compromise. The OWASP Top 10 for Large Language Model Applications form the foundation of our tests.

Prompt Injection & Jailbreaking

The most common problems with LLM applications are prompt injections and adversarial inputs. Attackers manipulate the model via crafted prompts: DAN attacks (Do Anything Now), token smuggling, multi-turn attacks, payload splitting across multiple chat turns, system prompt extraction via role-playing. Goal: Bypassing content filters, generating toxic/harmful content, bypassing safety guardrails.

PII Leakage & Training Data Extraction

Data leaks are an enormous problem: The model reveals private data it saw during training. Memorization attacks extract PII (names, emails, addresses), code snippets, proprietary business information via crafted prompts. Especially critical with fine-tuned models using customer data. GDPR compliance is also at risk with PII leakage - tools like Garak help with semi-automated detection.

Agent Hijacking & Excessive Agency

In complex LLM applications with autonomous agents (LangChain, AutoGPT, BabyAGI) the AI internally generates commands: Function calling exploits, tool injection (shell commands via LLM), tool chaining attacks (using multiple tools for RCE), sandbox escape via code interpreter, agent loop DoS. Risk: RCE (Remote Code Execution), complete system compromise, model theft.

Insecure Output Handling: XSS/SQLi via LLM

LLM outputs are passed unfiltered to backend systems: LLM generates XSS payloads for frontend (prompt injection → JavaScript code → stored XSS in chat interface), SQL injection via LLM-generated queries, SSRF via URL generation, command injection via shell calls. Risk: Classic web vulnerabilities arise from insecure processing of LLM responses.

RAG Poisoning & Vector DB Attacks

RAG systems (Retrieval-Augmented Generation) are vulnerable to document injection: Upload malicious PDF/DOCX with embedded instructions → vector DB (Pinecone/Weaviate/ChromaDB) becomes compromised. Risks: Cross-context information leakage (User A sees User B's data), privilege escalation via RAG context, embedding manipulation, knowledge base poisoning.

Model DoS & Cost Attacks

DoS attacks overwhelm the model: Token bombing (extremely long prompts), infinite loops via function calling, complex query DoS, agent loop exhaustion. Risk: Service unavailability + extremely high API costs (cost attack against OpenAI/Anthropic/Azure). An attacker can deliberately generate expensive queries → financial loss for the company.

Supply Chain & Model Theft

Supply chain vulnerabilities: Third-party models (Hugging Face), compromised plugins, outdated libraries (LangChain/LlamaIndex), API key leakage via dependencies. Model theft: Query-based extraction attacks → training a distilled model with similar performance, fine-tuning data leakage, watermark removal, membership inference attacks (testing if certain data was in training). Risk: IP theft, competitors gain access to proprietary models.

We're happy to check whether an attacker can steal or manipulate your LLM model.

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Related to LLM Pentesting

How much does a penetration test cost?

It's difficult to tell how much a test will cost because every test is individual - but we always find a solution that fits every budget.

Go to "Costs"

How often should you have a penetration test carried out?

The frequency of a pentest depends on various factors - here is an overview.

Go to "Frequency"

What types of penetration testing are there?

The word "penetration testing" is a very general word and can describe various types of testing. An overview is available here.

Go to "Types"

How does a penetration test usually work?

The process of a pentest is usually always the same - but the steps differ depending on the type of test.

Go to "Process"

Pentest: Services

Web Application Penetration Testing

A large part of the internet is based on websites and web applications.

API Penetration Testing

Modern websites and SPAs usually communicate with some kind of API.

Vulnerability Scanning

Fully automated vulnerability scanning for your IT infrastructure or application.

Our Tools & Methodology for LLM Pentests

We combine open-source red teaming frameworks with custom prompt injection chains and adversarial ML techniques - for maximum coverage from OWASP LLM Top 10 to zero-day discoveries.

Garak: Automated LLM Vulnerability Scanner

Garak is an open-source LLM scanner with 100+ probes for prompt injection, toxicity, PII leakage, jailbreaking detection. We use custom probes for specific use cases and combine them with manual multi-turn attacks.

  • Automated Prompt Injection Testing
  • Toxicity & Bias Detection
  • PII Leakage Probes (GDPR Compliance)

Custom Adversarial Prompts & DAN Attacks

Our own prompt injection libraries with 1000+ jailbreaking variants: DAN (Do Anything Now), token smuggling, payload splitting across multi-turn conversations, system prompt extraction via role-playing, Unicode/encoding bypasses.

  • DAN & Jailbreaking Variants
  • Multi-Turn Injection Chains
  • System Prompt Leaks via Crafted Inputs

Agent Testing: LangChain, AutoGPT, BabyAGI

Specialized tests for autonomous AI agents: Function calling exploits, tool injection (shell commands via LLM), sandbox escape detection, agent loop DoS, API key leakage via logs, chain-of-thought manipulation.

  • LangChain Tool Abuse Testing
  • Code Interpreter Sandbox Escapes
  • Excessive Agency Detection

RAG Poisoning & Vector DB Security

Tests for Retrieval-Augmented Generation systems: Document injection (PDF/DOCX with embedded instructions), vector DB manipulation (Pinecone/Weaviate/ChromaDB), cross-context information leakage, privilege escalation via RAG context.

  • Malicious Document Injection
  • Embedding Manipulation Testing
  • Cross-User Data Leakage Probes

Model Extraction & Training Data Leakage

Adversarial ML techniques: Query-based model extraction (distillation attacks), memorization testing (PII leakage from training data), membership inference attacks, model inversion, fine-tuning data extraction.

  • Query-Based Extraction Attacks
  • PII Memorization Probes
  • Membership Inference Testing

Cost Attack & Model DoS Testing

Resource exhaustion tests: Token bombing (extremely long prompts), infinite loops via function calling, complex query DoS, multi-turn context flooding, API cost attacks (OpenAI/Anthropic bill exploitation).

  • Token Bombing & Long Prompt DoS
  • Agent Loop Detection
  • Cost Attack Simulation

How Much Does an LLM Penetration Test Cost?

The price depends on complexity - number of features, agent architecture, RAG system size, fine-tuning status, and integration with backend systems significantly influence the scope.

Basic

Simple Chatbot Security Check

For simple LLM chatbots without agents

$3,800 - $6,500
2-4 Testing Days
  • OWASP LLM Top 10 Basic Coverage
  • Prompt Injection & Jailbreaking Tests
  • System Prompt Extraction Attempts
  • PII Leakage Detection (Basic)
  • Toxicity & Content Policy Bypasses
  • Insecure Output Handling (XSS/SQLi Checks)
  • Quick Ticket-Based Reporting
Ideal for: Simple chat interfaces, customer support bots, FAQ assistants without RAG/agents
Recommended

Full LLM + RAG + Agents Pentest

For complex GenAI systems with agents

$8,700 - $27,000
6-15 Testing Days
  • Complete OWASP LLM Top 10 Coverage
  • Advanced Prompt Injection Chains (Multi-Turn)
  • RAG Poisoning via Document Injection
  • Agent Hijacking & Tool Abuse (LangChain/AutoGPT)
  • Function Calling Exploits & Sandbox Escapes
  • Model Extraction & Training Data Leakage
  • Cost Attack & Model DoS Testing
  • Cross-Context Information Leakage (Multi-Tenant)
  • Supply Chain Analysis (Hugging Face/OpenAI/Anthropic)
  • Retest After Fix Phase + Management Summary
  • Optional: Guardrail Implementation Support
Ideal for: RAG systems, autonomous agents, document Q&A, enterprise LLM apps, fine-tuned models
Enterprise

LLM Red Teaming + Custom Model Training

For highly critical GenAI infrastructures

From $32,500
15-30+ Testing Days
  • Full-Scope Red Teaming (LLM + Infrastructure)
  • Custom Adversarial Model Training (TextFooler)
  • Multi-Model Interaction Testing (GPT-4 + Claude + Custom)
  • Advanced Model Extraction Attacks
  • Fine-Tuning Data Poisoning Simulation
  • Zero-Day Discovery Focus
  • Multi-Tenant Security Deep Dive
  • Compliance Support (AI Act, GDPR, ISO 42001)
  • Continuous Red Teaming (Quarterly/Yearly)
  • Guardrail Design & Implementation
  • LLM Security Training for Dev Teams
Ideal for: Enterprise GenAI platforms, multi-agent systems, critical infrastructure, fintech/healthcare LLMs

Depends on scope: The price is based on the number of LLM features (chat, RAG, agents, function calling), model type (OpenAI/Anthropic/custom), agent complexity, vector DB size, multi-tenancy, compliance requirements (AI Act, GDPR), and integration with backend systems. Special case: Custom model training for adversarial testing (TextFooler, fine-tuning) may incur additional costs. Note: LLM pentests should be combined with classic web app pentests for complete coverage.

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We're happy to check the security and integrity of your LLM model.

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Trust through experience

Some companies we have been able to help

We've had the privilege of working with some of the world's leading companies and strengthening their IT security.

Frequently Asked Questions

Which specific risks are examined in an LLM/GenAI penetration test?

It depends heavily on the app. The focus is on the OWASP LLM Top 10. If your app has a web frontend, classic web security vulnerabilities (XSS, SQLi, ...) quickly come into focus as well. Regarding the LLM, the focus is on data manipulation, prompt injections, and adversarial input attacks.

How long does an LLM/GenAI penetration test typically take?

We're usually finished within a week. But as always - it depends on the size of the entire application. The larger and more complex, the longer it takes. We've also seen tests that took several weeks to complete.

What does the documentation of an LLM/GenAI penetration test include?

In the case of LLM pentests, the report includes a management summary, technical details, and recommendations for action. Additionally, a large part of the report contains examples of how the system can be manipulated.

How often should an LLM system be tested?

Unlike classic applications that are tested, in the case of LLM it's often the case that the model is trained with the help of user data, sometimes on a weekly or even daily basis. Theoretically, you would have to repeat the entire LLM pentest for each new model generation.

How up-to-date is your testers' knowledge regarding the latest GenAI technologies?

In addition to hacking LLM technology, we also build systems based on LLMs for customers and for our internal systems. We know them from all perspectives and can therefore test them better.

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