What data looks like for this category
The data sources for code security auditing include source code repositories, dependency manifests, static application security testing (SAST) tool outputs, and public vulnerability databases. Data update rhythm matches code version release or project iteration cycles, usually once per version or per project. The generated documents include four types: code audit reports, vulnerability proof materials, remediation recommendation documents, and retest records. Core fields include vulnerability risk level, affected file paths, dangerous function call records, code snippets for remediation, and retest status. There are no fixed standardized unit requirements, and fields are adjusted based on specific audit projects.
What constraints do these characteristics impose on scheduled tasks and automation
Since data sources come from multiple types of tools and code repositories, scheduled tasks must adapt to pull permissions and update rhythms of different data sources to avoid excessive server resource usage from frequent pulls. Data update frequency is once per version or per project, so task trigger intervals must match this rhythm and should not be too frequent. The documents contain multiple types of heterogeneous fields, so automated summarization must complete field mapping and format unification, otherwise key audit information will be missing from reports. Core processing steps such as dangerous function localization and data flow tracing take a long time, so reasonable timeout thresholds and retry mechanisms must be set to avoid task unresponsiveness affecting online control of high-risk vulnerabilities.
How to configure settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Task trigger interval | 600–1440 minutes | Match the update rhythm of code security auditing once per version or per project, avoid frequent pulls from consuming resources |
| Task timeout threshold | 1800–3600 seconds | Cover time-consuming processing steps such as dangerous function localization and data flow tracing |
| Data source pull scope | Latest 20 code commits, current project dependency manifests | Avoid pulling excessive data that causes task timeouts, adapt to the scale of conventional audit projects |
| Report summary field mapping | Vulnerability level, affected file paths, code snippets for remediation | Match the core field requirements of code security audit documents, ensure complete summary content |
| Failure alert trigger conditions | Task execution timeout, missing core fields | Notify relevant personnel of exceptions in a timely manner, avoid high-risk vulnerabilities going online without audit |
| Task retry count | 2 times | Address temporary data source pull failures or model call timeout issues |
Three common configuration mistakes
- No output after scheduled task execution, with task status showing running but no results for a long time. This occurs when the data source pull scope is not limited, causing the pulled code repository or dependency manifest scale to exceed processing capacity.
- Service startup fails with an environment variable missing prompt. This happens when the required
AIPROXY_API_ENDPOINT(AI Proxy service root address) and non-emptyAIPROXY_API_TOKEN(AI Proxy admin token, must match the AI Proxy server configuration) are not configured on the App/Admin server, causing startup verification failure. It also occurs when the AI Proxy admin token is confused with other types of keys such as application API keys or model vendor secrets. - The summarized report lacks vulnerability proof or retest records. This is caused by not configuring the corresponding field mapping rules, and failing to associate the raw data output by SAST tools with the fields in audit documents.
How to verify successful configuration
- Check the startup logs of the App/Admin server to confirm that
AIPROXY_API_ENDPOINT(AI Proxy service root address) andAIPROXY_API_TOKEN(AI Proxy admin token) are correctly configured and non-empty. - Manually trigger a scheduled task, and check the task execution logs to confirm that expected data sources such as source code repositories and dependency manifests are successfully pulled.
- Review the generated summary report to confirm that it includes core fields such as vulnerability level and affected file paths, and that the format matches expectations.
- Simulate a task execution failure scenario, such as disconnecting the data source connection, to confirm that failure alerts trigger normally and are pushed to the specified channel.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.