Paper Citation Record · LEDGER

An LLM can Fool Itself: A Prompt-Based Adversarial Attack

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2310.13345.

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

pith.paper-citation-record.v1
2310.13345 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:58:57.524743Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

13
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0664db29-b933-4dc5-a11c-859a3ad58187 · inbound

TrustLLM: Trustworthiness in Large Language Models cites this paper.

TrustLLM: Trustworthiness in Large Language Models An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 271

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:17:08.780802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2c958187-762d-457b-9b89-bda4e45cd29a · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 269

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:08:35.801885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a89f0c1a-ecb1-4615-8f87-640891669099 · inbound

`Do as I say not as I do': A Semi-Automated Approach for Jailbreak Prompt Attack against Multimodal LLMs cites this paper.

`Do as I say not as I do': A Semi-Automated Approach for Jailbreak Prompt Attack against Multimodal LLMs An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T17:58:57.524743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66b63fee-6e24-4a2b-b768-5dd9fcfc0def · inbound

SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation cites this paper.

SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T13:54:58.012525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad3665db-4331-49e8-bf14-e2d05e012165 · inbound

CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models cites this paper.

CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:29.095286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20ea3c6d-f486-4fa1-a6b1-d8d183f748bc · inbound

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks cites this paper.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.137203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d036bc74-6729-4730-a8e7-344ed3dd9f4e · inbound

Stable Vision Concept Transformers for Medical Diagnosis cites this paper.

Stable Vision Concept Transformers for Medical Diagnosis An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:44.506141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c4fbc80-487a-42b6-afbd-7bf4c346500c · inbound

AI Agent Behavioral Science cites this paper.

AI Agent Behavioral Science An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 174

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:54.087989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a49bcc17-1f88-4835-9860-f7d9666e8067 · inbound

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images cites this paper.

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:43:35.300296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2b617af-1ddb-4b57-94bc-dcd7c3bea922 · inbound

Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI cites this paper.

Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:21:31.125849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 935b09e4-0305-4d31-b7ce-cf784eff5c55 · inbound

A comprehensive taxonomy of hallucinations in Large Language Models cites this paper.

A comprehensive taxonomy of hallucinations in Large Language Models An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T05:29:20.296271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94009e85-a9e6-40a2-bae6-8286cba4e480 · inbound

SATORI: Static Test Oracle Generation for REST APIs cites this paper.

SATORI: Static Test Oracle Generation for REST APIs An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T17:26:48.566674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4787619a-ca0e-4ceb-8121-ef48f21c05f6 · inbound

GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents cites this paper.

GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:05:26.669251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b00ef2a5-1dc9-46e1-9878-90a4131980d6 · inbound

Benign Overfitting in Adversarial Training for Vision Transformers cites this paper.

Benign Overfitting in Adversarial Training for Vision Transformers An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:21.061239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b6b36e91-b6ad-4cb9-8cbc-33b1de7fd6fb · inbound

PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures cites this paper.

PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:03:36.748253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 275b586b-3586-4265-8cde-f189cc396a80 · inbound

Distilling Safe LLM Systems via Soft Prompts for On Device Settings cites this paper.

Distilling Safe LLM Systems via Soft Prompts for On Device Settings An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.237616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97c10da7-4b51-4b51-821a-9ca4411c7f67 · inbound

Poller: Are LLMs Suitable for Evaluating the Poetry Understanding Task? cites this paper.

Poller: Are LLMs Suitable for Evaluating the Poetry Understanding Task? An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:04:21.525917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.