ARXIV:2602.21127 · AGENTIC SYSTEMS SECURITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

arXiv

A study on the vulnerabilities of human perception to deception by LLM-driven agents.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A study on the vulnerabilities of human perception to deception by LLM-driven agents.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A study on the vulnerabilities of human perception to deception by LLM-driven agents. However, this deepening trust introduces a novel attack surface: Agent-Mediated Deception (AMD), where compromised agents are weaponized against their human users.

METHOD

Full abstract

Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However, this deepening trust introduces a novel attack surface: Agent-Mediated Deception (AMD), where compromised agents are weaponized against their human users. While extensive research focuses on agent-centric threats, human susceptibility to deception by a compromised agent remains unexplored. We present the first large-scale empirical study with 303 participants to measure human susceptibility to AMD. This is based on HAT-Lab (Human-Agent Trust Laboratory), a high-fidelity research platform we develop, featuring nine carefully crafted scenarios spanning everyday and professional domains (e.g., healthcare, software development, human resources). Our 10 key findings reveal significant vulnerabilities and provide future defense perspectives. Specifically, only 8.6% of participants perceive AMD attacks, while domain experts show increased susceptibility in certain scenarios. We identify six cognitive failure modes in users and find that their risk awareness often fails to translate to protective behavior. The defense analysis reveals that effective warnings should interrupt workflows with low verification costs. With experiential learning based on HAT-Lab, over 90% of users who perceive risks report increased caution against AMD. This work provides empirical evidence and a platform for human-centric agent security research.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Specifically, only 8.6% of participants perceive AMD attacks, while domain experts show increased susceptibility in certain scenarios.

WHY NOW

Agentic Systems Security moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainA study on the vulnerabilities of human perception to deception by LLM-driven agents.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A study on the vulnerabilities of human perception to deception by LLM-driven agents.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.21127 · AGENTIC SYSTEMS SECURITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

arXiv

A study on the vulnerabilities of human perception to deception by LLM-driven agents.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A study on the vulnerabilities of human perception to deception by LLM-driven agents.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Agentic Systems Security moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainA study on the vulnerabilities of human perception to deception by LLM-driven agents.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A study on the vulnerabilities of human perception to deception by LLM-driven agents.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.21127

"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

A study on the vulnerabilities of human perception to deception by LLM-driven agents.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback78 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

3.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Agentic Systems Security moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A study on the vulnerabilities of human perception to deception by LLM-driven agents. However, this deepening trust introduces a novel attack surface: Agent-Mediated Deception (AMD), where compromised agents are weaponized against their human users.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However, this deepening trust introduces a novel attack surface: Agent-Mediated Deception (AMD), where compromised agents are weaponized against their human users.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Specifically, only 8.6% of participants perceive AMD attacks, while domain experts show increased susceptibility in certain scenarios.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agentic Systems Security moved forward this cycle; last verified April 2026. Public score 3.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Agentic Systems Security

Competitors

not indexed

Competitive landscape

A study on the vulnerabilities of human perception to deception by LLM-driven agents.

Segment

Agentic Systems Security

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.21127 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(78)

A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory

2025Qianshan Wei, Tengchao Yang et al.

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information

2025Xiao Zhan, J. Carrillo et al.

MPMA: Preference Manipulation Attack Against Model Context Protocol

2025Zihan Wang, Hongwei Li et al.

Prompt Injection Attack to Tool Selection in LLM Agents

2025Jiawen Shi, Zenghui Yuan et al.

The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections

2025Chaoran Chen, Zhiping Zhang et al.

Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms

2025Mutahar Ali, Arjun Arunasalam et al.

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

2025Bangbang Liu, Xinfeng Li et al.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges

2025Junyu Luo, Weizhi Zhang et al.

Open Deep Search: Democratizing Search with Open-source Reasoning Agents

2025Salaheddin Alzubi, Creston Brooks et al.

A Survey on Trustworthy LLM Agents: Threats and Countermeasures

2025Miao Yu, Fanci Meng et al.

PlanGenLLMs: A Modern Survey of LLM Planning Capabilities

2025Hui Wei, Zihao Zhang et al.

Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

2024Rui Ye, Xianghe Pang et al.

Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents

2024Yuyou Gan, Yong Yang et al.

XBRL Agent: Leveraging Large Language Models for Financial Report Analysis

2024Shijie Han, Haoqiang Kang et al.

Imprompter: Tricking LLM Agents into Improper Tool Use

2024Xiaohan Fu, Shuheng Li et al.

Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

2024Eike Schneiders, Tina Seabrooke et al.

Content, Nudges and Incentives: A Study on the Effectiveness and Perception of Embedded Phishing Training

2024Daniele Lain, Tarek Jost et al.

WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration

2024Yao Zhang, Zijian Ma et al.

AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

2024Zhaorun Chen, Zhen Xiang et al.

SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals

2024Ruihan Yang, Jiangjie Chen et al.

Showing 20 of 78 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisIntentional Deception as Controllable Capability in LLM Agents

2.0

Builds On ThisDon't Trust Stubborn Neighbors: A Security Framework for Agentic Networks

2.0

Commercially relevant

Higher ViabilityHow Adversarial Environments Mislead Agentic AI?

6.0

Higher ViabilityAgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks

5.0

Higher ViabilityDo No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling

4.0

Higher ViabilityYou Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents

6.0

Higher ViabilityToward Reliable, Safe, and Secure LLMs for Scientific Applications

4.0

Higher ViabilityDifferential Harm Propensity in Personalized LLM Agents: The Curious Case of Mental Health Disclosure

7.0

Higher ViabilityLying to Win: Assessing LLM Deception through Human-AI Games and Parallel-World Probing

7.0

Higher ViabilityPlant, Persist, Trigger: Sleeper Attack on Large Language Model Agents

7.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.21127
Route: /paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems
Active tab: read
Artifact: are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems/paper-pack
REST build passport API/api/v1/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "cbc8aa0c-7315-4c50-a14e-4be5a21a7f61",
  "arxiv_id": "2602.21127",
  "canonical_route": "/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems",
  "endpoints": {
    "paper_pack": "/api/v1/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems/paper-pack",
    "build_passport": "/api/v1/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

Canonical ID are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems | Route /paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.21127"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "\"Are You Sure?\": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems",
  "normalized_query": "2602.21127",
  "route": "/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems",
  "paper_ref": "are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: "Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

/buildability/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

Ignoreblocked

Subject: "Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

Paper ref

are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems

arXiv id

2602.21127

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

ea5138d59668c88ace5f8f0fe859b1736bbd082945842af75de86f7729b43944

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems#webpage",
      "url": "https://sciencetostartup.com/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems",
      "name": "\"Are You Sure?\": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems",
      "description": "A study on the vulnerabilities of human perception to deception by LLM-driven agents.",
      "isPartOf": {
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      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems#scholarlyArticle",
      "headline": "\"Are You Sure?\": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems",
      "description": "A study on the vulnerabilities of human perception to deception by LLM-driven agents.",
      "url": "https://sciencetostartup.com/paper/are-you-sure-an-empirical-study-of-human-perception-vulnerability-in-llm-driven-agentic-systems",
      "sameAs": "https://arxiv.org/abs/2602.21127",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.21127"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-24T17:23:11.000Z",
      "citation": [
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          "identifier": {
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            "propertyID": "SemanticScholar",
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        {
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        {
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"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

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