Seclens: Role-specific Evaluation of LLM's for security vulnerablity detection

Seclens: Role-specific Evaluation of LLM's for security vulnerablity detection | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID seclens-role-specific-evaluation-of-llm-s-for-security-vulnerablity-detection | Route /signal-canvas/seclens-role-specific-evaluation-of-llm-s-for-security-vulnerablity-detection

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Seclens: Role-specific Evaluation of LLM's for security vulnerablity detection

PDF: https://arxiv.org/pdf/2604.01637v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.820Z

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Watch and verify: Seclens: Role-specific Evaluation of LLM's for security vulnerablity detection

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Subject: Seclens: Role-specific Evaluation of LLM's for security vulnerablity detection

Verdict

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Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Existing benchmarks for LLM-based vulnerability detection compress model performance into a single metric, which fails to reflect the distinct priorities of different stakeholders.
Implicationpartial
Directly stated in the abstract as the motivation for the research
Verificationpartial
partial
Evidencepartial
we introduce SecLens-R, a multi-stakeholder evaluation framework structured around 35 shared dimensions grouped into 7 measurement categories.
Implicationpartial
Directly stated in the abstract with specific numbers
Verificationpartial
partial
Evidencepartial
The framework defines five role-specific weighting profiles: CISO, Chief AI Officer, Security Researcher, Head of Engineering, and AI-as-Actor.
Implicationpartial
Directly stated in the abstract with specific role names
Verificationpartial
partial
Evidencepartial
Each profile selects 12 to 16 dimensions with weights summing to 80, yielding a composite Decision Score between 0 and 100.
Implicationpartial
Directly stated in the abstract with specific numerical parameters
Verificationpartial
partial
Evidencepartial
We apply SecLens-R to evaluate 12 frontier models on a dataset of 406 tasks derived from 93 open-source projects, covering 10 programming languages and 8 OWASP-aligned vulnerability categories.
Implicationpartial
Directly stated in the abstract with specific numerical details
Verificationpartial
partial
Evidencepartial
Results show substantial variation across stakeholder perspectives, with Decision Scores differing by as much as 31 points for the same model.
Implicationpartial
Directly stated in the abstract with specific numerical result
Verificationpartial
partial
Evidencepartial
Qwen3-Coder achieves an A (76.3) under the Head of Engineering profile but a D (45.2) under the CISO profile
Implicationpartial
Directly stated in the abstract with specific model performance data
Verificationpartial
partial
Evidencepartial
These findings demonstrate that vulnerability detection is inherently a multi-objective problem and that stakeholder-aware evaluation provides insights that single aggregated metrics obscure.
Implicationpartial
Directly stated conclusion in the abstract, though slightly more interpretive
Verificationpartial
partial

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Seclens: Role-specific Evaluation of LLM's for security vulnerablity detection

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