LLM Penetration Testing
Specialized security testing for large language model implementations, including prompt injection, data leakage, and model manipulation assessments.
Large Language Models have become integral to modern applications, powering everything from customer support chatbots and code generation assistants to document analysis systems and decision support tools. However, the unique architecture of LLMs — their reliance on prompt-based interactions, autoregressive token generation, and training on vast internet-scale datasets — creates an entirely new class of security vulnerabilities. Unlike traditional software where inputs are validated against strict schemas, LLMs process natural language inputs that can be crafted to circumvent safety guardrails, extract sensitive training data, or produce harmful outputs. The probabilistic nature of these models means that identical inputs can sometimes produce different outputs, making security testing both more challenging and more critical than traditional application security assessment.
The LLM threat landscape is extensively documented through frameworks such as the OWASP Top 10 for LLM Applications and the MITRE ATLAS knowledge base, encompassing risks ranging from prompt injection and jailbreaking to model denial of service and supply chain vulnerabilities. Prompt injection attacks remain the most prevalent threat, where adversarial inputs are crafted to override system instructions and manipulate model behavior. Model inversion and extraction attacks attempt to reconstruct training data, potentially exposing sensitive information embedded in the model during training. Jailbreaking techniques continue to evolve, with attackers developing increasingly sophisticated methods to bypass safety alignments through role-playing, encoding tricks, and multi-turn conversational manipulation. Understanding and mitigating these threats requires specialized expertise that combines deep knowledge of both cybersecurity and natural language processing.
Our LLM Penetration Testing methodology is built on years of hands-on experience testing hundreds of LLM deployments across diverse industries and use cases. We employ a structured, multi-phase approach that begins with reconnaissance and threat modeling to understand your specific LLM architecture, deployment context, and security requirements. Our testing combines automated scanning using proprietary tools and custom-built adversarial attack frameworks with manual deep-dive testing by experienced AI security researchers. We test every attack surface including the model API, prompt interface, output processing pipeline, training data pipeline, and model deployment infrastructure. Our comprehensive test suite covers all major LLM attack categories, ensuring thorough coverage of the OWASP Top 10 for LLMs and beyond.
The outcome of our engagement provides a complete understanding of your LLM security posture with clear, actionable remediation guidance. You will receive a detailed penetration testing report documenting all discovered vulnerabilities with attack narratives, proof-of-concept demonstrations, and business impact analysis. Our findings are prioritized using a customized risk scoring framework that accounts for both the technical severity of vulnerabilities and the specific business context of your LLM deployment. Beyond the report, we provide hands-on remediation support including hardened system prompts, input sanitization libraries, output validation filters, monitoring configurations, and architectural recommendations. With our assessment, your organization can deploy LLM-powered applications with confidence that they are resilient against current and emerging attack techniques.
How We Deliver
A structured methodology refined through hundreds of successful engagements.
Reconnaissance & Threat Modeling
We begin by mapping your LLM architecture, identifying all components including the model endpoint, prompt templates, context sources, output processing pipeline, and supporting infrastructure. Threat modeling is performed using frameworks such as STRIDE and LLM-specific threat taxonomies to identify potential attack vectors and prioritize testing activities based on business risk.
Prompt Injection & Jailbreaking
A comprehensive suite of prompt injection and jailbreaking techniques is executed against your LLM deployment. We test direct injection through user-facing interfaces, indirect injection through retrieved context and tool outputs, and multi-turn injection strategies. Our jailbreak testing covers encoding-based, role-based, and logic-based bypass techniques against your model's safety alignment.
Data Leakage & Extraction Testing
We systematically test for unintended data leakage through model outputs, including training data extraction via membership inference and extraction attacks, prompt-based leakage of system instructions and context data, and probing for memorized sensitive information. We also assess whether error messages and debug outputs expose internal system details.
Model Security Control Assessment
The effectiveness of your model security controls is evaluated including output filtering and content moderation systems, rate limiting and abuse detection mechanisms, input sanitization and preprocessing pipelines, and guardrail enforcement at the application and infrastructure layers. We test whether controls can be bypassed or degraded under adversarial conditions.
Remediation & Hardening Validation
All findings are documented with detailed technical descriptions and prioritized remediation recommendations. We provide hardened prompt templates, input sanitization and output validation patterns, security configuration guidance, and monitoring and detection rule sets. We validate critical fixes through retesting to confirm effective remediation before production deployment.
What You Receive
Every engagement delivers actionable insights and tangible outcomes.
LLM Penetration Test Report
Comprehensive report documenting all discovered vulnerabilities including attack chains, proof-of-concept payloads, severity ratings using a customized LLM risk scoring framework, and detailed remediation guidance for each finding with code-level recommendations and configuration changes.
Hardened System Prompts
A set of hardened system prompt templates designed to resist injection attacks while maintaining model effectiveness. Includes defense-in-depth prompt structures, instruction hierarchy implementations, and prompt component patterns that enforce security boundaries between user and system instructions.
Detection & Monitoring Rules
Custom detection rules for identifying LLM attacks in production, including prompt injection attempts, jailbreak probes, data extraction patterns, and abuse indicators. Rules are provided for integration with SIEM platforms, API gateways, and observability tools with alert thresholds and incident response playbooks.
Security Control Validation Suite
A reusable test suite containing automated security tests for continuous validation of LLM security controls. Tests cover prompt injection resistance, jailbreak prevention, output filtering effectiveness, rate limiting, and abuse detection, designed for integration into your CI/CD pipeline.
Key Benefits
Partner with SecureNexGen for results that matter.
Comprehensive Attack Coverage
Systematic testing against the full spectrum of LLM attack techniques as defined by OWASP Top 10 for LLMs and MITRE ATLAS, ensuring no attack vector is left unexplored and your defenses are validated against real-world adversarial techniques.
Real-World Attack Simulation
Our testing simulates realistic attack scenarios based on current threat intelligence and adversary TTPs, providing an accurate assessment of how your LLM deployment would withstand actual attacks from motivated adversaries.
Regulatory Compliance Support
Assessment findings are mapped to regulatory requirements including EU AI Act transparency and risk management obligations, NIST AI RMF security controls, and sector-specific AI governance frameworks, supporting your compliance documentation and audit readiness.
Production-Ready Hardening
Beyond identifying vulnerabilities, we deliver production-ready hardening artifacts including hardened prompts, security configurations, and monitoring rules that can be immediately deployed to improve your LLM security posture without disrupting existing functionality.
What's Covered
Comprehensive scope designed to leave no stone unturned.
Frequently Asked Questions
Common queries about our service delivery and process.
What is the difference between prompt injection and jailbreaking?
How do you test for training data extraction from LLMs?
What LLM models and deployment architectures do you support?
How do you assess the business impact of LLM vulnerabilities?
How often should LLM penetration testing be performed?
Ready to Get Started?
Contact our team to discuss your requirements and receive a tailored proposal.