> For the complete documentation index, see [llms.txt](https://docs.snyk.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snyk.io/agent-security/evo-by-snyk/ai-spm/risk-intelligence.md). # Risk intelligence The **Risk intelligence agent** scores the AI models in your inventory so you can judge their risk. Snyk tests each model independently across five security categories using hundreds of test suites, rather than relying on self-reported vendor claims. ## Risk index The Risk index is a score from 0 to 1000 that measures the severity-weighted failure rate of a model across Snyk's security tests. Higher scores indicate greater risk. A model with critical vulnerabilities scores higher than a model with only low-severity issues, even at a similar pass rate. The index is absolute. A Risk index of 300 represents a 30% weighted failure rate, and the score does not change when Snyk adds new models. As Snyk tests new models and updates its assessments, Risk index scores update automatically. A Risk index of 300 or above in a category raises a high-severity issue for the model. To learn how Evo raises and tracks issues, visit Policies and issues. ## Security categories Snyk scores each model in five categories: * Insecure code generation: how often the model generates vulnerable code, for example, SQL injection, cross-site scripting (XSS), operating system command injection, and path traversal. * Bias and discrimination: whether the model produces discriminatory or stereotyping content across gender, race, and religion. * Attack reconnaissance: whether the model reveals system prompts or internal configuration through extraction and adversarial escalation. * Safety guardrail bypass: how easily a user circumvents the model's safety guardrails to produce harmful or policy-violating content. * Sensitive data exposure: whether the model reproduces personally identifiable information (PII) from its training data. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.snyk.io/agent-security/evo-by-snyk/ai-spm/risk-intelligence.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.