AI Hallucination

Why Language Models Hallucinate

Language models generate the most likely next words based on patterns in their training data. When the model lacks the information it needs, it can fill the gap with something that fits the pattern but is not true, and present it with the same confidence as a correct answer.

Hallucinations in Security Work

• Invented findings. A model reports a vulnerability in code that is actually safe, adding to false positive noise.
• Wrong dismissals. A model explains away a real issue with reasoning that sounds right but is not.
• Nonexistent packages. Coding assistants suggest dependencies that do not exist, and attackers register those names with malicious code.
• Incorrect fixes. A patch that looks correct but does not close the vulnerability or breaks something else.

OWASP lists misinformation, with hallucination as a major cause, among the top 10 risks for LLM applications.

How to Reduce Hallucinations

• Give the model the evidence it needs, or tools to look it up, instead of asking it to recall.
• Require the model to cite specific evidence for its conclusions.
• Verify output independently, with tests, builds or a second review.
• Treat uncertainty as a reason to escalate, not to guess.
• Check that every suggested dependency exists and comes from a trusted source.

Proof Beats Confidence

The most reliable defense is not a better prompt but a requirement for proof. A claim that a vulnerability is real is stronger with a working exploit. A claim that a fix works is stronger when the original attack fails against it.

How Nullify Guards Against Hallucinations

Nullify only dismisses findings with evidence, validates findings on reachability, exploitability and business context before a human sees them, and independently re-reviews each exploit before it is reported. Proposed fixes are checked against builds and tests before a PR opens. See how Nullify proves vulnerabilities.