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ARXIV:2604.18874 · AGENT ROBUSTNESS · SUBMITTED 22 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.18874AGENT ROBUSTNESSSUBMITTED 22 APR · 20:30 UTCFRESHNESS STALEZhonghao Zhan · Huichi Zhou · Zhenhao Li · Peiyuan Jing · Krinos Li · Hamed Haddadi · arXiv
This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap.
Opportunity summary
Pain This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap. Yet this very reliance creates a critical attack surface.
Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. AEI constitutes environmental deception: constructing a "fake world" of poisoned search results and fabricated reference networks around unsuspecting agents. Code availability is flagged in…
Agent Robustness moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap.
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Paper Pack
10.48550/arXiv.2604.18874This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap.
Abstract
Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking "can the agent use tools correctly" but never "what if the tools lie". We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a "fake world" of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via POTEMKIN, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities.
Source availability
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Extraction status
Parse run linkedA document parse run is attached to this paper.
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 6.0
PROBLEM
This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap. Yet this very reliance creates a critical attack surface.
METHOD
Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. AEI constitutes environmental deception: constructing a "fake world" of poisoned search results and fabricated reference networks around unsuspecting agents. Code availability is flagged in the production...
WHY NOW
Agent Robustness moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 17, "author": "Zhonghao Zhan; Huichi Zhou; Zhenhao Li; Peiyuan Jing; Krinos Li; Hamed Haddadi", "title": "How Adversarial Environments Mislead Agentic AI?"
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partial
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Concepts
Methods
Materials
Markets
Competitors
This research introduces a framework to test the vulnerability of tool-using AI agents to deceptive tool outputs, revealing a significant robustness gap.
Segment
Agent Robustness
Adoption evidence
No public code link in the paper record yet
Commercial read
6.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Extension
Commercially relevant
Conflicting
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2/3 checks · 67%
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.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Derived signals show verified:false until source-backed receipts exist.
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
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
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
No observed cost estimate is verified.
Evidence
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Gaps
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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
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 CRM or outreach source attached.
People
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Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
Next verification path
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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BUZZ
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