AI Model & LLM Security
Protect your AI applications, LLM integrations, and deployed models from prompt injection, data leakage, and model-specific attacks. As more teams build on top of LLMs, these tools add the security layer that general-purpose security scanners weren't designed for.
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AI Model & LLM Security Tools
Building an application on top of a large language model introduces security risks that traditional tools weren't built to handle — prompt injection, where a user tricks the model into ignoring its instructions; data leakage, where the model reveals sensitive information from its training or context; and model evasion, where attackers manipulate inputs to produce harmful outputs.
What these tools specifically address
- Prompt injection detection — catching inputs designed to override a model's system prompt or guardrails.
- Output scanning — checking model responses for sensitive data, harmful content, or policy violations before they reach users.
- Model inventory and access control — tracking what models are in use across an organization and who has access to them.
Why this category is growing fast
As companies deploy more AI agents that take real-world actions — sending emails, querying databases, modifying files — the consequences of a security failure increase significantly. This is still a young market, but security teams at companies building on LLMs are starting to treat it as a distinct risk category, not just a general application security problem.
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