What Data for This Category Looks Like
This category of data is produced by project security response teams, maintainers, and security researchers. Daily documents include security advisories, vulnerability reports, fix patches, and CVE submission materials. Data sources include private vulnerability reports, project code repositories, publicly available information from CVE numbering authorities, and official release channels. Data is updated per single event, and each document contains fields such as vulnerability ID, impact scope, patch links, disclosure time, and affected versions. Unlike supply chain security and open source dependency governance, this category focuses on the public disclosure and remediation process of vulnerabilities in the project itself.
What Constraints Do These Characteristics Impose on Context and Token Workflows
Data for this category is updated per single event, and includes both private and public information. This requires that context be isolated per individual vulnerability handling task, to avoid cross-event data mixing and private information leaks. Long-text vulnerability reports and CVE submission materials consume significant token counts, so a maximum token limit per single reference must be set to prevent exceeding model supported limits. Additionally, the impact assessment phase requires rapid association of historical vulnerability data. Excessive context recall increases token consumption and task duration. If processing times out, patches may be released prematurely, harming project reputation.
How to Configure
The following configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum number of context entries | 3–5 entries | Data for this category is updated per event, and only historical vulnerability records for the same project need to be associated per event. Excessive entries will cause token limits to be exceeded |
| Maximum token count per single reference | 1000–1500 token | Vulnerability reports and CVE submission materials are often long texts. Limiting per-reference tokens prevents overall token overflow |
| Context data source scope | Only public vulnerability data + private reports authorized by the project | Prevent unauthorized leakage of private vulnerability information, in compliance with security disclosure specifications |
| Context recall matching threshold | Calibrated based on actual testing | Balance recall accuracy and token consumption, adapt to differences in impact scope across vulnerabilities |
| Task timeout threshold | 600 seconds | Covers the normal execution duration of the most time-consuming phases, including vulnerability impact assessment and patch preparation |
Three Common Misconfigurations
- Phenomenon: An operator configures 30 context entries in the FastGPT v4.17.0 AI node, but only 2 historical context entries appear in the conversation details, and the generated response fails to associate corresponding vulnerability data. Cause: Context storage is not divided by event dimension. The system accumulates context across global conversations by default, retaining only the latest small amount of conversation content.
- Phenomenon: The large language model returns an error for exceeding context token count, or truncates the generated result. Cause: The system does not limit the maximum token count per single reference, and an operator references the complete long-text vulnerability report directly, exceeding the token limit supported by the model.
- Phenomenon: Task execution times out during the vulnerability impact assessment phase, with no return results. Cause: The system does not split long-text vulnerability reports into reasonable fragments, and does not configure a batch processing threshold for context recall, resulting in excessive data loading in a single task.
How to Confirm Configuration Is Complete
- View the AI node configuration page in FastGPT v4.17.0, and confirm that values for configuration items such as Maximum number of context entries and Maximum token count per single reference match preset values.
- Import a historical vulnerability report as test data, initiate an associated query, and verify that the number of context entries displayed in the conversation details matches the configured value.
- Submit a CVE submission material containing long text, check that the generated response associates historical data for the corresponding vulnerability, and no token overflow error occurs.
- Simulate multiple consecutive vulnerability handling tasks, confirm that task execution does not time out, and context does not mix across tasks.
The values provided here are common starting points and should be evaluated against the operator’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.