AI Security Vulnerabilities

AI agents and LLMs are exposed to a category of security vulnerabilities that doesn’t exist in traditional software. Because these systems interpret natural language instructions and generate free-form responses, they can be manipulated through carefully crafted inputs. No code exploits required.

Understanding AI security vulnerabilities

Unlike traditional software bugs, AI security vulnerabilities arise from the model’s ability to follow instructions, generate content, and access tools. An attacker doesn’t need to find a buffer overflow or SQL injection; they can simply craft a prompt that tricks the model into revealing its system instructions, generating harmful content, or performing unauthorized actions.

These vulnerabilities are categorized separately from business logic failures (like hallucination or omission) because they involve deliberate exploitation rather than accidental errors. However, both categories should be tested together as part of a comprehensive evaluation strategy.

The OWASP Top 10 for LLM Applications provides a widely referenced framework for classifying these risks. Giskard’s automated red teaming scan tests for these vulnerabilities using 55+ specialized attack probes.

Tip

You can find examples of security vulnerabilities in our RealHarm dataset ↗.

Types of security vulnerabilities

Prompt Injection A security vulnerability where malicious input manipulates the model's behavior or extracts sensitive information.

Harmful Content Generation Production of violent, illegal, or inappropriate material by AI models.

Information Disclosure Revealing internal system details, training data, or confidential information.

Output Formatting Issues Manipulation of response structure for malicious purposes or poor output formatting.

Robustness Issues Vulnerability to adversarial inputs or edge cases causing inconsistent behavior.

Stereotypes & Discrimination Biased responses that perpetuate harmful stereotypes and discriminatory behavior.

Getting started with AI security testing

Detecting these vulnerabilities requires a combination of automated scanning and targeted red teaming. Start with an automated scan to establish a baseline, then build a test dataset that covers the vulnerability categories most relevant to your use case.

AI Red Teaming Scan Scan your agent for vulnerabilities with 55+ specialized attack probes.