What Data for This Category Looks Like
Data for this category mainly comes from source code repositories, dependency lists, static code scanning tools, and public vulnerability databases. Data update rhythm follows project version iterations or security audit cycles, and is usually updated once per version or project. Core documents include code audit reports, vulnerability proof documents, repair recommendation documents, and retest records. Fields include vulnerability severity, affected function paths, dependent component versions, repair status, and no unified fixed units, varying by document type.
What Constraints These Characteristics Impose on the Model Integration and Configuration Link
Multi-source heterogeneous data input requires model integration to support different formats of code snippets, vulnerability descriptions, and dependency information. Frequently updated audit data requires configurations to support dynamic adjustment of context length, to avoid analysis interruptions caused by data overflow. High computational consumption in dangerous function localization and data flow tracing links requires model parameters to adapt to long-text processing capabilities. At the same time, the potential consequences of errors in high-risk vulnerability audits require configurations to have stable call fault-tolerance mechanisms, to avoid audit process failure caused by configuration errors.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text input requirements of code security auditing, covering complete function context and data flow tracing content |
maxTokens | 4000 token | Limits the length of single-round model output, avoids parsing failures caused by overlong results, and adapts to the generation requirements of audit reports and repair suggestions |
quoteMaxToken | 1500 token | Limits the length of referenced code snippets, ensuring that the model can accurately locate affected dangerous functions and code paths |
temperature | 0.2–0.4 | Reduces random generation probability, improving the accuracy and consistency of vulnerability analysis and repair suggestions |
AIPROXY_API_ENDPOINT | Fill in the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions paths, and do not fill in single-model service addresses | Required environment variable for the App/Admin server. After configuration, a network connection to the AI Proxy service can be established, adapting to the security audit scenario of private deployment |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy server configuration, and it cannot be empty | Required environment variable for the App/Admin server. Completes identity verification with the AI Proxy service, ensuring the security of interface calls |
Three Common Misconfigurations
- Incorrectly configuring the
AIPROXY_API_ENDPOINTserver environment variable, filling in the wrong AI Proxy service root address such as appending extra paths or filling in a single-model service address. Returns a502 Bad Gatewayerror during model calls, or service startup verification fails. Troubleshooting steps: Verify the actual root address of the AI Proxy service, confirm that no extra paths such as/v1or/chat/completionsare appended. - Incorrectly configuring the
AIPROXY_API_TOKENserver environment variable, filling in a token that does not match the AI Proxy server configuration or leaving it empty. Returns a401 Unauthorizederror during model calls, and vulnerability analysis results cannot be generated. Troubleshooting steps: Verify the administrator token configured on the AI Proxy server, confirm that the token is not empty and matches the server configuration. - Setting
maxContextto a value exceeding 15000 tokens, causing model call timeouts and audit task stagnation. This occurs because the long-text processing limit of code security auditing is not adapted to, exceeding the context length threshold supported by the model.
How to Confirm Successful Configuration
- Verify the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables configured on the App/Admin server, confirm thatAIPROXY_API_ENDPOINTis the root address of the AI Proxy service, andAIPROXY_API_TOKENis a non-empty administrator token consistent with the server configuration. - Initiate a test call with a small code snippet, check whether the vulnerability analysis result returned by the model includes dangerous function localization content, to verify whether the configuration takes effect.
- After adjusting the
temperatureparameter, compare the model outputs of two identical inputs, confirming that the consistency of the results meets expectations. - Check the system operation logs, confirm that there are no
401or50xlevel model call errors, verifying the stability of the configuration.
The values provided are common starting points and should be evaluated 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.