Securing Your LLMs: Top 5 Vulnerabilities (OWASP Top 10 for LLMs)

Key takeaways: Large Language Models connected to critical systems expose enterprises to five critical security vulnerabilities identified in the OWASP Top 10 for LLMs. Prompt injection, the most dangerous flaw, allows attackers to manipulate model instructions to bypass safeguards, enabling arbitrary code execution and data theft. Insecure output handling creates XSS vulnerabilities when LLM-generated code is executed directly in browsers without sanitization. Training data poisoning lets attackers inject biases or backdoors into fine-tuned models through compromised external datasets. Model denial of service exploits LLMs' resource-intensive nature through complex requests designed to overload servers and inflate costs. Sensitive information disclosure risks revealing API keys, personal data, or secrets present in training data or conversation context. Defense strategies include strict input-output separation, treating all LLM outputs as untrusted data, rigorous dataset provenance verification, rate limiting with cost caps, and implementing output filters to detect sensitive patterns. Ikasia advises never deploying an LLM to production without a defense-in-depth security strategy.

Insecurity by Design?

Large Language Models (LLMs) are by nature non-deterministic and difficult to control. When connected to critical systems or sensitive data, they expose the company to new risks. OWASP has published a Top 10 specific to LLMs. Here are the 5 most critical ones.

1. Prompt Injection

This is the king of flaws. An attacker manipulates the model's instructions to make it bypass its safeguards.

• Risk: Arbitrary code execution, data theft, generation of hateful content.
• Defense: Strictly separate system instructions from user data, use clear delimiters, and validate inputs.

2. Insecure Output Handling

Blindly trusting an LLM's output is dangerous. If the LLM generates JavaScript that is executed directly in a user's browser, it is an XSS (Cross-Site Scripting) flaw.

• Defense: Treat LLM outputs as untrusted data. Encode, sanitize, and validate before any use.

3. Training Data Poisoning

If you fine-tune a model on external data, an attacker can "poison" this data to introduce biases or backdoors.

• Defense: Rigorously verify the provenance and integrity of training datasets (Supply Chain Security).

4. Model Denial of Service (DoS)

LLMs are resource-intensive. An attacker can send complex requests designed to overload the server and blow up the bill.

• Defense: Strict rate limiting, cost capping, and request timeouts.

5. Sensitive Information Disclosure

The model can reveal secrets (API keys, personal data) present in its training data or in the conversation context.

• Defense: Data Sanitization and implementation of output filters to detect sensitive patterns (emails, credit card numbers).

Conclusion

LLM security is a rapidly evolving battlefield. Never deploy a model in production without a defense-in-depth strategy.