Power User Ops Eval V1
Mend.io AI-native AppSec Platform Power User Ops Eval · Mend.io
Operator-facing eval focused on evidence traceability, handoff quality, noise discipline, and workflow automation for AppSec operations.
Sample tests· showing 3 of 25
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | evidence traceability sast Security engineer needs to explain to the development team why a specific SAST finding was flagged as critical. The audit trail must show the dataflow path, taint sources, and rule that triggered the finding. | should_escalate: false · resolution_label: document · risk_level: low | Pass / Fail |
| 02 | evidence traceability sca AppSec lead preparing for security review needs to produce a complete dependency audit showing vulnerable packages, their transitive paths, and remediation status across all repositories. | should_escalate: false · resolution_label: document · risk_level: low | Pass / Fail |
| 03 | evidence traceability ai bom Compliance officer needs to generate an AI Bill of Materials (AI-BoM) for regulatory submission showing all AI frameworks, models, and their risk assessments across the organization. | should_escalate: false · resolution_label: document · risk_level: low | Pass / Fail |
How this eval is graded
Evaluate whether the response provides adequate evidence traceability, produces complete and decision-ready handoffs, maintains appropriate noise discipline without suppressing real vulnerabilities, and follows effective automation practices for developer productivity.
Pass threshold: a criterion passes at a judge score of 4 or higher.
Rubric criteria
- Evidence Traceability
- Handoff Quality
- Noise Discipline
- Workflow Automation
Recommended for
Works with
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