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Cogent Risk Assessment And Contextual Prioritization

Cogent Platform & Cogent Community · Cogent Security

Agentic AI Vulnerability Management — Cogent Security

Evaluates Cogent Security's Risk Assessment & Contextual Prioritization across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Agentic AI Vulnerability Management eval coverage.

About Cogent Security

Cogent Security builds agentic AI for vulnerability management. The Cogent Platform runs Triage, Risk Assessment, Remediation, and Verification agents on a real-time data foundation — investigating findings, correlating assets to owning teams, prioritizing by real exploitability over raw CVSS, driving remediation through engineering workflows, and validating that fixes actually happened. The free Cogent Community surface pairs VulnCheck-powered CVE intelligence with a customizable Discover Feed and an AI Research Assistant that produces cited, plain-language deep-dives.

Employees

~30

Industry

AI Security / Vulnerability Management

Headquarters

San Francisco, CA

Sample tests· showing 3 of 9

#InputExpected behaviorCheck
01

Finding A: CVSS 9.8, no public exploit, internal-only host behind two VPN hops. Finding B: CVSS 7.5, KEV-listed with mass-exploitation underway, on an internet-facing edge service.

Per Cogent's stated principle — agents 'evaluate real exploitability in the environment, not just CVSS severity' — Finding B must rank above Finding A. The explanation must cite KEV listing, mass-exploitation telemetry, and exposure (internet-facing vs internal-only) as the dominant inputs, with CV…

Pass / FailAi Platformcritical
02

Risk Assessment Agent assigns priority P1 to a finding but the underlying exploit-availability evidence is partial (PoC code exists in a private channel; reachability analysis incomplete).

Per Cogent's documented 'full explainability and confidence levels' for every prioritization decision, the agent must surface both the verdict and the confidence (low/medium/high or numeric) and explain what evidence is missing. Low-confidence P1 must be visually distinct from high-confidence P1 in…

Pass / FailAi Platformhigh
03

A CVE on a database engine. The instance is in a private subnet with no ingress and credentialed access only from a hardened bastion.

Risk Assessment Agent must factor environment-specific exposure (network reachability, auth posture, segmentation) into its verdict, not just the abstract CVE properties. The explainability artifact must show the exposure evidence (subnet, security-group, ingress rules) so the engineer can verify.

Pass / FailAi Platformhigh

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How this eval is graded

Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

Rubric criteria

  • Cogent
  • Ai Platform
  • Risk Assessment And Contextual Prioritization

Recommended for

Cogent Platform & Cogent CommunityCogent Security customers

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Frequently asked questions

What does the Cogent Risk Assessment And Contextual Prioritization eval for Cogent Security Cogent Platform & Cogent Community test?+

Evaluates Cogent Security's Risk Assessment & Contextual Prioritization across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Agentic AI Vulnerability Management eval coverage.

How is the Cogent Risk Assessment And Contextual Prioritization eval scored?+

The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

How many test cases does this eval pack include?+

The Cogent Risk Assessment And Contextual Prioritization pack for Cogent Security Cogent Platform & Cogent Community contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.

How do I run this eval?+

Sign up for Corsac, connect your model or agent endpoint, and run the Cogent Risk Assessment And Contextual Prioritization pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.

Run this eval in your workspace

Connect your data, configure thresholds, and review results with your team.