How to Pentest LLMs Like a Security Researcher Cybersecurity

LLM Security in the Modern Threat Landscape

Large Language Models have become integral to business operations, but their security posture remains poorly understood across most organizations. This comprehensive session brings together security practitioners to examine how LLMs differ fundamentally from traditional web applications when it comes to attack surface, vulnerability assessment, and exploitation techniques. The discussion bridges the gap between classical penetration testing methodologies and the emerging discipline of AI-focused security research, providing both theoretical foundations and practical insights that apply to real-world scenarios where AI systems handle sensitive data or critical operations.

Reconnaissance and Enumeration for AI Systems

The first critical phase of LLM penetration testing involves determining whether a target is actually running a real Language Model or simply serving static, pre-recorded responses. This reconnaissance stage differs markedly from traditional web application testing because attackers must identify behavioral patterns that reveal genuine AI inference happening on the backend. Security researchers explore techniques for profiling LLM behavior, examining response variability, testing system prompts through careful query construction, and identifying which model variant or fine-tuned version might be running. Understanding the difference between a real LLM and a chatbot facade with hardcoded responses fundamentally changes the attack strategy and determines which vulnerability classes are even relevant to test.

Prompt Injection as the Primary Attack Vector

Prompt injection represents the most direct and consequential vulnerability class in LLM security testing. Unlike SQL injection or command injection in traditional applications, prompt injection exploits the natural language interface that makes LLMs powerful in the first place. Attackers craft carefully constructed inputs designed to override the original system instructions, redirect the model's behavior, or extract protected information hidden in the system prompt itself. The session demonstrates practical examples showing how seemingly innocent user inputs can fundamentally alter what an LLM outputs, potentially causing it to ignore safety guardrails, reveal confidential training data, or perform actions the system was designed to prevent. This vulnerability becomes even more dangerous when LLMs integrate with external APIs or databases, as injected prompts can manipulate these connections to access unauthorized resources.

Hallucinations and Model Unreliability as Security Issues

Hallucinations—where LLMs confidently generate false information—create a distinct security challenge that doesn't map cleanly onto traditional vulnerability categories. When defenders rely on LLM outputs for decision-making without verification mechanisms, hallucinations become exploitable weaknesses. An attacker might craft prompts designed to trigger specific hallucination patterns, causing the system to generate fabricated data about users, products, or security status. The session explores how hallucinations interact with other vulnerabilities: a prompt injection attack combined with hallucination behavior can generate plausible-sounding but completely fabricated sensitive information that downstream systems might trust. This highlights why simple output filtering proves insufficient—defenders must implement architectural patterns that prevent LLMs from being the single source of truth for critical decisions.

Excessive Agency and Insecure AI Workflow Patterns

As LLMs evolve toward autonomous agents capable of taking actions beyond text generation, the attack surface expands dramatically. Excessive agency occurs when systems grant LLMs permission to modify databases, execute API calls, access file systems, or perform other consequential actions based on user prompts with minimal validation. In this context, prompt injection transforms from an information disclosure vulnerability into a remote code execution equivalent. The discussion examines how insecure segmentation between user input, system instructions, and action authorization creates pathways for lateral privilege escalation within AI systems. When an LLM can simultaneously receive user input and execute API calls, even indirect prompt injection—where attacker-controlled data appears in context windows—becomes critical. Real-world examples demonstrate how weak validation in AI workflows allows attackers to chain multiple seemingly-minor vulnerabilities into complete system compromise.

Information Disclosure and API Integration Risks

LLM applications frequently integrate with external services, databases, and APIs to provide context and take actions. These integrations exponentially increase the attack surface because they create multiple pathways for information disclosure. An attacker might use prompt injection to manipulate API calls, causing the system to leak authentication credentials, access control tokens, or sensitive database contents that the LLM shouldn't normally expose. The session explores how model fine-tuning on sensitive datasets, inadequate data sanitization in training pipelines, and poor segmentation between different user contexts can all lead to information leakage through seemingly innocent LLM responses. Defenders must implement strict controls around what data reaches the model, implement robust output filtering based on user context, and architect systems where LLMs operate on principle of least privilege—accessing only the minimum data necessary for legitimate operations.

Practical Lab-Based Exploitation and Defense Strategies

The most actionable portion of this session involves hands-on demonstrations and lab walkthroughs showing how prompt injection exploits work in practice and how defenders can identify and mitigate these issues. Participants learn to recognize indicators of compromise, test for common misconfigurations, and implement practical defense strategies without requiring massive security infrastructure. The discussion emphasizes that securing LLM applications isn't fundamentally about novel technology—it applies time-tested principles like input validation, output encoding, least privilege access, and secure architecture patterns. However, the specific implementation details differ substantially because natural language interfaces resist traditional whitelist-based filtering approaches. Effective defense requires understanding the specific behaviors of the LLM being used, implementing contextual access controls, maintaining audit logs of model inputs and outputs, and validating that LLM behavior matches expectations before automating responses into critical systems.

Applicability Across Security Roles and Career Paths

This content serves penetration testers seeking to expand into AI security, bug bounty hunters looking for emerging vulnerability classes with less competition, AI security researchers building specialized knowledge, AppSec professionals protecting LLM-based applications, red teamers developing attack methodologies, and developers building LLM applications who need to understand defensive requirements. Security leaders exploring AI risk will find practical threat modeling information applicable to their organizations. The session acknowledges that LLM security testing requires different mindsets and techniques than traditional application security, making it an excellent specialization for professionals looking to differentiate their expertise or organizations attempting to secure their AI investments before threats mature into widespread exploitation.