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Paper Citation Record · LEDGER

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents

As of 22 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.19837.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.19837 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:38:21.167591Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved36
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 439a50fc-99b9-4e87-8b3e-ef0aa8567205 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Toolformer: Language models can teach themselves to use tools,

Reference 1

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source=pdf_text observed=2026-08-01T11:38:17.724576Z digest=sha256:86ac39719bea7ec992590a251f022eee5eb6bda0ddb49b2fe31c8afa430420d5

Observation 406f13a8-d145-472d-abd3-0c4fea9fd29a · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents ReAct: Synergizing Reasoning and Acting in Language Models

Reference 2

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source=pdf_text observed=2026-08-01T11:38:17.811439Z digest=sha256:ee27cb46e8247371249a4b4dc649a66a2dcab6963863c9bdb3157b2525803f58

Observation 3835fcb9-75e7-43d8-9357-b1e3b5998c37 · outbound

This paper cites Webarena: A realistic web environ- ment for building autonomous agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Webarena: A realistic web environ- ment for building autonomous agents,

Reference 3

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source=pdf_text observed=2026-08-01T11:38:17.873374Z digest=sha256:5472bb14e66d0eb31f67e3738e33697b46e5e4bc837578f43142b7ffe125128a

Observation dc5d88c4-e067-4ab8-a17b-266a9a27e4f4 · outbound

This paper cites Hug- ginggpt: Solving ai tasks with chatgpt and its friends in hugging face,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Hug- ginggpt: Solving ai tasks with chatgpt and its friends in hugging face,

Reference 4

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source=pdf_text observed=2026-08-01T11:38:17.939940Z digest=sha256:0602cd711489e5ec209e72c5f646f8f5dcb9301fb07be54d7a08f7f5fd277f66

Observation 4324c514-e183-449d-85ee-2f56c0bfff77 · outbound

This paper cites Openhands: An open platform for ai software developers as generalist agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Openhands: An open platform for ai software developers as generalist agents,

Reference 5

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Observation ef692a33-3a5a-4d1e-8e4f-32f774fed009 · outbound

This paper cites Not what you’ve signed up for: Compromising real- world llm-integrated applications with indirect prompt injection,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Not what you’ve signed up for: Compromising real- world llm-integrated applications with indirect prompt injection,

Reference 6

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source=pdf_text observed=2026-08-01T11:38:18.073366Z digest=sha256:1fe35dbe3d64acdc6261f9f8a7e30986ae1be554ee69d47fb438a641d9a93cc2

Observation b8224f54-7a93-4d77-907b-630c8fd2f9e3 · outbound

This paper cites OW ASP Top 10 for Large Language Model Applications,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents OW ASP Top 10 for Large Language Model Applications,

Reference 7

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source=pdf_text observed=2026-08-01T11:38:18.157562Z digest=sha256:47da5eba55332e1f71899fcb5385ecf9f636aade6620d5b386f2c342633b24f2

Observation 69c3665a-794d-4bbf-a3a7-5398b763c3f3 · outbound

This paper cites Prompt injection attacks against gpt-3,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Prompt injection attacks against gpt-3,

Reference 8

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source=pdf_text observed=2026-08-01T11:38:18.279577Z digest=sha256:95d67278298e785234caaaf34a88589bb95b97c258f57e3ff0ed6b1c396c6e82

Observation 0f285f95-910b-465d-acc6-52ad24d85793 · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Ignore Previous Prompt: Attack Techniques For Language Models

Reference 9

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source=pdf_text observed=2026-08-01T11:38:18.391594Z digest=sha256:bc0e8eb313da8489bf7e3e011edecbd8bf5040d4864d8835ee0c8ebe87ce3e22

Observation 1d34a9f1-220a-4668-9a11-9b2259266236 · outbound

This paper cites Agentvigil: Automatic black-box red-teaming for indirect prompt injection against llm agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Agentvigil: Automatic black-box red-teaming for indirect prompt injection against llm agents,

Reference 10

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source=pdf_text observed=2026-08-01T11:38:18.505283Z digest=sha256:1102a2f8f3e8a61d7ce5c9a7d516a6d5176179e2b39137f9a32e00b4c978a4be

Observation 24eae479-604f-4232-bf5d-39ced6fdad31 · outbound

This paper cites Topicattack: An indirect prompt injection attack via topic transition,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Topicattack: An indirect prompt injection attack via topic transition,

Reference 11

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source=pdf_text observed=2026-08-01T11:38:18.597411Z digest=sha256:93f5bd5942bdd1ac74a04680853f525b2c32cdf7974a8e0484a4f643ce25d991

Observation c1f18558-62f8-4566-9ddf-8fca0cd700df · outbound

This paper cites Agentx- ploit: End-to-end red-teaming for ai agents powdered by multi-agent systems.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Agentx- ploit: End-to-end red-teaming for ai agents powdered by multi-agent systems

Reference 12

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source=pdf_text observed=2026-08-01T11:38:18.703530Z digest=sha256:4a330289f9c7418d817a7ee75968b991c0a082edf12076ec07fd39c3924bba85

Observation d091e421-e7d3-4936-ab85-ce9eda9ccf9b · outbound

This paper cites Adaptools: Adaptive tool-based in- direct prompt injection attacks on agentic llms,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Adaptools: Adaptive tool-based in- direct prompt injection attacks on agentic llms,

Reference 13

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source=pdf_text observed=2026-08-01T11:38:18.773275Z digest=sha256:41718cfdb0ecda37013fe4140b1b09df6afd84edca67f4930e84d4563c078b3c

Observation 74d790ad-8cb5-4176-bda6-496a1c82b9f4 · outbound

This paper cites Security challenges in ai agent deployment: Insights from a large scale public competi- tion,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Security challenges in ai agent deployment: Insights from a large scale public competi- tion,

Reference 14

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source=pdf_text observed=2026-08-01T11:38:18.917301Z digest=sha256:bffeb735d470964d0df2bf3b1cb23eae6b066a9fe61819d6aeccb10de31ab62e

Observation 16c1cd37-6130-40e8-95af-1498e1c15a93 · outbound

This paper cites "real attackers don’t compute gradients.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents "real attackers don’t compute gradients

Reference 15

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source=pdf_text observed=2026-08-01T11:38:18.974397Z digest=sha256:fa5cc77fdbee383cabf7bb57815384386be2f97126ef8252a3c500cbe6f7df22

Observation 509b499f-e1b6-4b29-a5c4-111513f6c1fd · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Prompt Injection attack against LLM-integrated Applications

Reference 16

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source=pdf_text observed=2026-08-01T11:38:19.117826Z digest=sha256:c7167fb70b114a2a24097ea826126989b56702e39b0cf9c02676f939955a124f

Observation 3d7e5468-3fd0-4549-9d6d-d937b507e4f5 · outbound

This paper cites Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents,

Reference 17

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source=pdf_text observed=2026-08-01T11:38:19.156471Z digest=sha256:bcad82547b7e5e4a4a741c10ae3680127df7a495664e8e5a86e4e6a07a11f42f

Observation c3b1ab49-46b0-4d2e-b6ef-2942b7526974 · outbound

This paper cites InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Reference 18

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Observation 05a35f45-341e-4709-92d2-1921aee36735 · outbound

This paper cites ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents

Reference 19

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source=pdf_text observed=2026-08-01T11:38:19.374306Z digest=sha256:bd289bf9506dca017a53b09edd0a8b134467139150df55b81fca2858f693ab3f

Observation 884ac497-cf9b-4395-bf92-330b86f12931 · outbound

This paper cites Prompt Injection Attack to Tool Selection in LLM Agents.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Prompt Injection Attack to Tool Selection in LLM Agents

Reference 20

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source=pdf_text observed=2026-08-01T11:38:19.451019Z digest=sha256:167500473b328681e8108f8a3deef82f25755527e40eec0a5c27f2017c9c51b0

Observation 10044431-dfe8-496f-a514-cc53671bdb1a · outbound

This paper cites Obliinjection: Order-oblivious prompt injection attack to llm agents with multi-source data,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Obliinjection: Order-oblivious prompt injection attack to llm agents with multi-source data,

Reference 21

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source=pdf_text observed=2026-08-01T11:38:19.527776Z digest=sha256:fe6e09261e9c6da82c17788789186d87716c43e30c3a6be7523bcae587648918

Observation 1b17e9f1-3daf-4a2b-ae26-da210751b368 · outbound

This paper cites AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents

Reference 22

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source=pdf_text observed=2026-08-01T11:38:19.674889Z digest=sha256:94f23296c1814c6b68efe38f022e417735d71bc6af94b508357c6e0bad5d5770

Observation 6d20f8c5-8837-485e-9eb0-d048eb27d4b3 · outbound

This paper cites Autohijacker: Automatic indirect prompt injection against black-box LLM agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Autohijacker: Automatic indirect prompt injection against black-box LLM agents,

Reference 23

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source=pdf_text observed=2026-08-01T11:38:19.816027Z digest=sha256:3282ab0328967895e20d3fe3e115fdb5c44e2a5277f7ef8ec3af4409ac09e668

Observation 6f47252f-d67d-4d46-8782-9986e52a8ca1 · outbound

This paper cites Agentxploit: End-to-end red-teaming for AI agents powdered by multi-agent systems,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Agentxploit: End-to-end red-teaming for AI agents powdered by multi-agent systems,

Reference 24

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source=pdf_text observed=2026-08-01T11:38:19.894752Z digest=sha256:d05fa9478e1b3601176b029d85bc8b48d1a42fb73650a3e46cef2f79e14f79d0

Observation 974b7850-a8c9-4c18-9a0c-1e312a6b0300 · outbound

This paper cites Tooltweak: An attack on tool selection in llm-based agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Tooltweak: An attack on tool selection in llm-based agents,

Reference 25

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source=pdf_text observed=2026-08-01T11:38:19.997439Z digest=sha256:79b0b11eed56e89572d11441734996f2d2c8fd191136a15c2c4146f2bf2a09e9

Observation 2f16feac-0b42-4e09-95a7-4bc030cc665c · outbound

This paper cites Verigrey: Greybox agent validation,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Verigrey: Greybox agent validation,

Reference 26

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source=pdf_text observed=2026-08-01T11:38:20.114753Z digest=sha256:e10efb769cdfcb72b8f8cce1ab413645351c32449fe484deeb4a0102f2929387

Observation 00f2b1d0-3081-4e5a-bca5-0a832b70c9c2 · outbound

This paper cites SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

Reference 27

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source=pdf_text observed=2026-08-01T11:38:20.224853Z digest=sha256:ff8ff7658c153a3f08a4bb6d5b8a2efb44a093c4c98fa74a1b8bb500935e5590

Observation 1f54a5ea-3e5e-4731-9dbf-5e8c0f3b8ecd · outbound

This paper cites Udora: A unified red teaming framework against llm agents by dynamically hijacking their own reasoning,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Udora: A unified red teaming framework against llm agents by dynamically hijacking their own reasoning,

Reference 28

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source=pdf_text observed=2026-08-01T11:38:20.324753Z digest=sha256:49242f5e8c855ae5c5d7a83a1d1dabc9f22930cda0a8de993b1bdb59cc3395fd

Observation 506ddbd4-b000-4ee3-b561-3aacd36afb6c · outbound

This paper cites Bench- marking and defending against indirect prompt injection attacks on large language models,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Bench- marking and defending against indirect prompt injection attacks on large language models,

Reference 29

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source=pdf_text observed=2026-08-01T11:38:20.414827Z digest=sha256:82b2afe4e4934b7abfe8cee8ebc8f205ef56dc6bca6c09c2435597118f869597

Observation f29c388e-4120-4db7-8372-d5d81442e719 · outbound

This paper cites Agent security bench (asb): Formalizing and benchmark- ing attacks and defenses in llm-based agents,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Agent security bench (asb): Formalizing and benchmark- ing attacks and defenses in llm-based agents,

Reference 30

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source=pdf_text observed=2026-08-01T11:38:20.545074Z digest=sha256:ff72fa1ab8531ed69465641ea91c783f0636d103cf0ad2b11c87b0d69dcb9b31

Observation 617e5efd-51f7-4608-ba58-8137230741e6 · outbound

This paper cites Universal and Context-Independent Triggers for Precise Control of LLM Outputs.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Universal and Context-Independent Triggers for Precise Control of LLM Outputs

Reference 31

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source=pdf_text observed=2026-08-01T11:38:20.754753Z digest=sha256:876da993f32eddcb68daffccb52f45f00bfe963867aeb8cae7da769888a04d02

Observation 9df13c94-2b54-4595-8378-68e2012e0a89 · outbound

This paper cites Trojan’s whisper: Stealthy manipulation of openclaw through injected bootstrapped guidance,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Trojan’s whisper: Stealthy manipulation of openclaw through injected bootstrapped guidance,

Reference 32

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source=pdf_text observed=2026-08-01T11:38:20.844901Z digest=sha256:ec58fbc0b9edb2efc1d3a11361731b8e67b6f6282ff44789cf68fbbf62f367b0

Observation 4c3c54a5-a722-4862-8002-5e1b296c0bc4 · outbound

This paper cites Openclaw docs,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Openclaw docs,

Reference 33

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source=pdf_text observed=2026-08-01T11:38:20.934753Z digest=sha256:03124e6f5695363c5894c3624d4d54d504b78eded8d188b7978959f33f219bea

Observation eb9eb1ff-679c-4e82-b893-34f5e6c0727a · outbound

This paper cites Fine-tuned deberta-v3-base for prompt injection detection,.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Fine-tuned deberta-v3-base for prompt injection detection,

Reference 34

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source=pdf_text observed=2026-08-01T11:38:21.054852Z digest=sha256:76d2ada3ce2f1d0ae26ba67612f3d8cd6c56e721d950d8dfde296d856307128d

Observation 1a8d5faf-b4fd-4723-adea-a3a4f4635572 · outbound

This paper cites Defending Against Indirect Prompt Injection Attacks With Spotlighting.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents Defending Against Indirect Prompt Injection Attacks With Spotlighting

Reference 35

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source=pdf_text observed=2026-08-01T11:38:21.167591Z digest=sha256:caae5c14658e827b62c0920b9fcbc161ffaad674fe99b35a751d19cd391db346

Observation 0bf5d298-f643-4924-88c2-b4ec411cc881 · outbound

This paper cites "Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice.

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents "Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice

Reference 2022

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source=pdf_text observed=2026-08-01T11:38:19.047650Z digest=sha256:5716d5b54a7dad15dd839bfd9e6b4c87d278f1b461c1042d7d93322b960b87e8

Pith citing papers

No inbound Pith citation observations are available.