> 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/scan-fix-and-prevent/scan-with-snyk/snyk-api-web/review-and-fix/severity-levels-in-findings.md). # Severity levels in findings The severity levels of Snyk API & Web findings Snyk API & Web assigns a severity level to each finding to summarize its overall risk based on the following factors: * The likelihood of the vulnerability being found and exploited. * The skills required to exploit the vulnerability. * The impact of exploiting the vulnerability. For example, a vulnerability that is easy to find, easy to exploit, and has a high impact is likely classified with a high severity. Different findings for the same vulnerability can have different severity levels depending on the context in which Snyk API & Web finds the vulnerabilities. Multiple factors can influence this context, which Snyk API & Web considers to lower or raise the severity level. For example, the severity of a finding can be higher or lower depending on whether the scanned target has authentication configured. ## Severity levels The following table describes the different severity levels. | Severity | Description | Examples | | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | | Critical | These findings require immediate attention and remediation due to their potentially devastating impact. | SQL Injection, OS Command Injection | | High | These findings can have a direct impact on the application security, either clients or service owners, for example, by granting the attacker access to sensitive information. | Reflected Cross-Site Scripting, Path Traversal | | Medium | Medium findings do not usually have an immediate impact alone, but combined with other findings, can lead to a successful compromise of the application. | Cross-Site Request Forgery, Unencrypted Communications | | Low | Findings where either the exploit is not trivial, or the finding cannot be exploited by itself. | Directory Listing, Clickjacking | ## Related information Visit [Interpret target scan results](/scan-fix-and-prevent/scan-with-snyk/snyk-api-web/review-and-fix/interpret-target-scan-results.md) for more information about findings. --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.snyk.io/scan-fix-and-prevent/scan-with-snyk/snyk-api-web/review-and-fix/severity-levels-in-findings.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.