LLM Security: Technical Deep-Dive
For security professionals and developers. 7 minute read.
Prompt Injection
The #1 LLM vulnerability. Attackers craft inputs that override the developer's instructions. Like SQL injection, but for AI. Example: "Ignore previous instructions and..." works because the model can't distinguish trusted vs untrusted text.
OWASP LLM Top 10
The Open Web Application Security Project published the top 10 LLM risks: Prompt Injection, Insecure Output Handling, Training Data Poisoning, Model Denial of Service, and more.
Data Leakage
LLMs may reveal sensitive information from their training data or current context. Attackers use indirect questioning, encoding tricks, or role-play to extract data the AI should protect.
Jailbreaking
Bypassing an AI's safety guidelines through clever prompting. Techniques include DAN (Do Anything Now), role-playing scenarios, and hypothetical framing to make the AI ignore its rules.
Context Manipulation
Exploiting how LLMs process context. Attackers inject hidden instructions in documents, use encoding (base64, ROT13), or manipulate conversation history to influence AI behavior.
Defense Strategies
Input sanitization, output filtering, system prompt hardening, separation of concerns (don't give LLMs access to sensitive data), rate limiting, and human-in-the-loop for sensitive operations.