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ARXIV:2603.17639 · AGENTS · SUBMITTED 19 MAR · 21:58 UTC · FRESHNESS STALE
ARXIV:2603.17639AGENTSSUBMITTED 19 MAR · 21:58 UTCFRESHNESS STALEarXiv
VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation.
Opportunity summary
Pain VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation. A Large Language Model (LLM) agent involves one or more LLMs in the back-end.
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In the front end, it conducts autonomous decision-making by combining the LLM outputs with results obtained by invoking several external tools. A public repository…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation.
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Paper Pack
10.48550/arXiv.2603.17639VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation.
Abstract
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end. In the front end, it conducts autonomous decision-making by combining the LLM outputs with results obtained by invoking several external tools. The autonomous interactions with the external environment introduce critical security risks. In this paper, we present a grey-box approach to explore diverse behaviors and uncover security risks in LLM agents. Our approach VeriGrey uses the sequence of tools invoked as a feedback function to drive the testing process. This helps uncover infrequent but dangerous tool invocations that cause unexpected agent behavior. As mutation operators in the testing process, we mutate prompts to design pernicious injection prompts. This is carefully accomplished by linking the task of the agent to an injection task, so that the injection task becomes a necessary step of completing the agent functionality. Comparing our approach with a black-box baseline on the well-known AgentDojo benchmark, VeriGrey achieves 33% additional efficacy in finding indirect prompt injection vulnerabilities with a GPT-4.1 back-end. We also conduct real-world case studies with the widely used coding agent Gemini CLI, and the well-known OpenClaw personal assistant. VeriGrey finds prompts inducing several attack scenarios that could not be identified by black-box approaches. In OpenClaw, by constructing a conversation agent which employs mutational fuzz testing as needed, VeriGrey is able to discover malicious skill variants from 10 malicious skills (with 10/10= 100% success rate on the Kimi-K2.5 LLM backend, and 9/10= 90% success rate on Opus 4.6 LLM backend). This demonstrates the value of a dynamic approach like VeriGrey to test agents, and to eventually lead to an agent assurance framework.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
partial0 refs; 0 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation. A Large Language Model (LLM) agent involves one or more LLMs in the back-end.
METHOD
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In the front end, it conducts autonomous decision-making by combining the LLM outputs with results obtained by invoking several external tools. A public repository is linked, so build verification can ins...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation. A Large Language Model (LLM) agent involves one or more LLMs in the back-end.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In the front end, it conducts autonomous decision-making by combining the LLM outputs with results obtained by invoking several external tools. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Materials
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VeriGrey is a grey-box testing framework that uncovers security risks in LLM agents through dynamic prompt mutation.
Segment
Agents
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
Direct
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Unknown
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CITED BY
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1/3 checks · 33%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 50% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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