What data for this category looks like
Penetration testing service test case and defect analysis data comes from target system interface logs, public vulnerability databases, automated scanner output results, and manual verification records from red team engineers. Data is updated per individual project or major system version. Each project data is associated with four types of documents: test authorization letters, penetration test reports, vulnerability reproduction records, and rectification suggestions. Data fields include vulnerability CVE IDs, reproduction steps, risk levels, authorization scopes, repair suggestions, and more. Field content adjusts dynamically based on project scenarios, with no fixed unified unit format.
Constraints imposed on model integration and configuration
The characteristics of penetration testing service data impose multiple constraints on the model integration and configuration link. First, data is updated per project or system version and linked to test authorization letters. Configurations must support project-level isolation of keys and context to avoid cross-project data leaks or risks of mismatched authorization scopes. Second, data includes two types of heterogeneous content: automated scan results and manual verification records. Configurations must support parameter settings for multi-source data integration, to ensure the model can combine both types of information to complete vulnerability verification. Third, unstructured vulnerability reproduction and rectification suggestion content requires the model's context window to adapt to the large data volume of a single project, to avoid analysis interruptions caused by context overflow.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the unstructured data volume of a single project penetration test, avoiding context overflow |
maxTokens | 2000–4000 token | Limits the output length of single-round vulnerability analysis, ensuring report content is concise and compliant |
toolChoice | auto | Allows the model to automatically select tools to integrate scan results and manual verification records |
temperature | 0.3–0.5 | Reduces output randomness, ensuring the rigor of vulnerability ratings and rectification suggestions |
AIPROXY_API_TOKEN | Configured as the administrator token consistent with the AI Proxy server and cannot be empty | Serves as the authentication credential for the AI Proxy server, ensuring normal service connection |
Three common configuration mistakes
- Phenomenon: When calling the model,
Api response error: http://aiproxy:3000 access failedis returned. Troubleshooting items:AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, which may cause model connectivity failure or service startup verification failure. - Phenomenon: After configuring the model to turn off the thought output switch, the generated result still contains
<think>tag content. Cause: The output format is not forcibly constrained in the prompt, and thetoolChoiceparameter does not correctly restrict the model's thinking logic. - Phenomenon: When using the knowledge base conversation API key to call the model, an unauthorized prompt is still displayed. Troubleshooting items:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or an incorrect application API key is used for authentication.
How to confirm configuration is complete
- Check that the server has correctly configured the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables, with no startup verification failure records. - Import a single penetration test report fragment, verify that the model can correctly extract core fields such as vulnerability CVE ID and reproduction steps.
- Configure a test case generation task, check whether the output meets the format requirements of penetration test documents, with no redundant or incorrect content.
- Check system logs to confirm there are no error records such as model call timeouts or parameter abnormalities.
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.