What the data for this category consists of
Daily documents for penetration testing services include test authorization letters, penetration test reports, vulnerability reproduction records, and rectification suggestions. Data sources cover target systems, public vulnerability databases, automated scanner results, and manual attack verification records. Data updates follow a cycle of individual projects or model version iterations. Field differences across documents are significant: test authorization letters include approval workflow and permission scope fields. Penetration test reports include vulnerability severity and impact scope fields. Vulnerability reproduction records include attack paths and exploit code snippets. The core difference between this category and general vulnerability scanning services is the inclusion of manually verified attack chain analysis content. The data structure is relatively complex, with non-standard manual record sections.
Constraints imposed on model integration and configuration
The data characteristics of penetration testing services impose multiple constraints on model integration and configuration. First, multi-source, non-standard document structures require models to support adaptation to different input formats. Context length parameters must be configured specifically for this use case. Second, project-based data update rhythms mean model calls must bind to project context. Global fixed call rules are not recommended. Third, manually verified attack chain content requires high output rigor. Random generation parameters for the model must be limited. Fourth, the sensitivity of compliance materials requires call permissions to be strictly bound to project authorization. Independent keys and endpoint configurations must be used to secure access.
Recommended configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Penetration test reports are typically lengthy, full project context must be loaded to ensure review accuracy |
maxTokens | 2000–3000 token | Sufficient length is required for summarizing vulnerability reproduction records and rectification suggestions, to cover core technical details |
temperature | 0.2–0.4 | Compliance review requires precise output, avoid randomly generated inconsistent content, ensure consistency of vulnerability ratings and rectification suggestions |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000 | Access endpoint for the AI Proxy service configured for the App/Admin server, do not append routing paths such as /v1, do not configure as a single model address |
AIPROXY_API_TOKEN | Non-empty administrator token matching the AI Proxy server configuration | Access credential for the AI Proxy service configured for the App/Admin server, to secure inter-service communication permissions |
toolChoice | auto | Automatically adapt to different types of review materials, handle multiple document types such as authorization letters and reports without manual tool switching |
Three common misconfigurations
- Incorrect configuration of the App/Admin server environment variable
AIPROXY_API_TOKEN(must match the AI Proxy server configuration and be non-empty) leads to permission errors during service startup verification or model calls. The root cause is credentials not matching the AI Proxy server verification rules. - Accidental entry of
AIPROXY_API_ENDPOINTin the FastGPT model configuration interface, or incorrect configuration of the App/Admin server environment variableAIPROXY_API_ENDPOINT(must be the AI Proxy service root address, do not append routing paths such as/v1, do not configure as a single model address) leads to abnormal model call responses. The root cause is endpoint configuration not meeting requirements. - When a code execution module is used in a workflow to call a model, failure to correctly configure streaming response parsing logic leads to non-segmented result returns. The root cause is failure to adapt to the model's streaming response format.
How to confirm successful configuration
- A section of test authorization letter approval content is input into the FastGPT model test interface, and the model’s correct identification of the permission scope field and generation of a compliance judgment are verified.
- A model call is made to generate a summary of a single vulnerability reproduction record, and the output length is checked against the
maxTokensconfiguration range. - The FastGPT server environment variable configuration is reviewed, confirming that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly set and meet requirements. - A workflow call to the model bound to the project context is triggered, and the expected generation of a penetration test compliance review report is verified.
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.