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ARXIV:2604.18658 · AI AGENT SAFETY · SUBMITTED 22 APR · 21:32 UTC · FRESHNESS STALE
ARXIV:2604.18658AI AGENT SAFETYSUBMITTED 22 APR · 21:32 UTCFRESHNESS STALEDongcheng Zhang · Yiqing Jiang · arXiv
Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors.
Opportunity summary
Pain Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug…
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We quantify the defense gap on two benchmarks: a compositional safety system achieves 100% TPR / 0% FPR on AgentHarm (generic criminal harm) yet…
AI Agent Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors.
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Paper Pack
10.48550/arXiv.2604.18658Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors.
Abstract
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug 2024), Microsoft 365 Copilot calendar-injection leaks (Jan 2024), and a Meta agent unauthorized forum post exposing operational data (Mar 2026). We propose Owner-Harm, a formal threat model with eight categories of agent behavior damaging the deployer. We quantify the defense gap on two benchmarks: a compositional safety system achieves 100% TPR / 0% FPR on AgentHarm (generic criminal harm) yet only 14.8% (4/27; 95% CI: 5.9%-32.5%) on AgentDojo injection tasks (prompt-injection-mediated owner harm). A controlled generic-LLM baseline shows the gap is not inherent to owner-harm (62.7% vs. 59.3%, delta 3.4 pp) but arises from environment-bound symbolic rules that fail to generalize across tool vocabularies. On a post-hoc 300-scenario owner-harm benchmark, the gate alone achieves 75.3% TPR / 3.3% FPR; adding a deterministic post-audit verifier raises overall TPR to 85.3% (+10.0 pp) and Hijacking detection from 43.3% to 93.3%, demonstrating strong layer complementarity. We introduce the Symbolic-Semantic Defense Generalization (SSDG) framework relating information coverage to detection rate. Two SSDG experiments partially validate it: context deprivation amplifies the detection gap 3.4x (R = 3.60 vs. R = 1.06); context injection reveals structured goal-action alignment, not text concatenation, is required for effective owner-harm detection.
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Proof status
unverified0 refs; 3 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
Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug 2024), Microsoft 365 C...
METHOD
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the g...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We quantify the defense gap on two benchmarks: a compositional safety system achieves 100% TPR / 0% FPR on AgentHarm (generic criminal harm) yet only 14.8% (4/27; 95% CI: 5.9%-32.5%) on AgentDojo injectio...
WHY NOW
AI Agent Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 15, "author": "Dongcheng Zhang; Yiqing Jiang", "title": "Owner-Harm: A Missing Threat Model for AI Agent Safety", "creation date": null, "modification date": null
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Concepts
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Introducing Owner-Harm, a new threat model and benchmark for AI agent safety, with a proposed defense system that significantly improves detection of deployer-harming behaviors.
Segment
AI Agent Safety
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
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CITED BY
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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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
0 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
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, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
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
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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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
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No GTM owner verified.
No CRM or outreach source attached.
People
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Gaps
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Regulatory need unclassified.
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People
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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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