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Paper Citation Record · LEDGER

OET: Optimization-based prompt injection Evaluation Toolkit

As of 17 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2505.00843.

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

pith.paper-citation-record.v1
2505.00843 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:38:39.701686Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:30:14.284471Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T06:17:41.897796Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved28
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b50062e-16b0-477a-833b-d26e5bb769a7 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

OET: Optimization-based prompt injection Evaluation Toolkit Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 4

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source=pdf_text observed=2026-08-16T04:38:39.294695Z digest=sha256:c053131617fdb3ae0ae7fc2a6093858bdef187a955189dd6582eba7ccccc62cb

Observation e7c1170b-fa30-47c5-8ca8-36d4fe4c9503 · outbound

This paper cites SecAlign: Defending Against Prompt Injection with Preference Optimization.

OET: Optimization-based prompt injection Evaluation Toolkit SecAlign: Defending Against Prompt Injection with Preference Optimization

Reference 5

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source=pdf_text observed=2026-08-16T04:38:39.301351Z digest=sha256:ee59b34aa2b3858affeaced95064be518e1c795a001214ce52bfd28f72785474

Observation d11c2963-3eff-4c48-aefa-488e53718a3c · outbound

This paper cites The Llama 3 Herd of Models.

OET: Optimization-based prompt injection Evaluation Toolkit The Llama 3 Herd of Models

Reference 8

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source=pdf_text observed=2026-08-16T04:38:39.323097Z digest=sha256:c358f3748b33ac37ba80275575ee583f2b0599c4366503472fc7ce77d574174f

Observation 4d6006f2-0e8f-4cfe-9107-a73599998ffc · outbound

This paper cites Attacking Large Language Models with Projected Gradient Descent.

OET: Optimization-based prompt injection Evaluation Toolkit Attacking Large Language Models with Projected Gradient Descent

Reference 9

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source=pdf_text observed=2026-08-16T04:38:39.361751Z digest=sha256:0f8dc9183686544ab477c241187d1d78612aa4beb105d85ac716087353100b0d

Observation 28c559f0-be82-4119-b624-c85f8fa41fb6 · outbound

This paper cites GPT-4o System Card.

OET: Optimization-based prompt injection Evaluation Toolkit GPT-4o System Card

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:38:39.384048Z digest=sha256:7c36c385444cf46610b9a2859b09005b13ef167c607337ae61edd6948a20a393

Observation 0bbce21e-25d1-402f-8516-961b96ee7259 · outbound

This paper cites An In-Depth Investigation of Data Collection in LLM App Ecosystems.

OET: Optimization-based prompt injection Evaluation Toolkit An In-Depth Investigation of Data Collection in LLM App Ecosystems

Reference 11

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source=pdf_text observed=2026-08-16T04:38:39.393918Z digest=sha256:d46ebcd2234e837edbacbbf1acade460d7f27079945ca693228d8bf3016c769d

Observation 91770c3a-9538-4f21-a3c3-b4ce00af6187 · outbound

This paper cites PromptKeeper: Safeguarding System Prompts for LLMs.

OET: Optimization-based prompt injection Evaluation Toolkit PromptKeeper: Safeguarding System Prompts for LLMs

Reference 12

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source=pdf_text observed=2026-08-16T04:38:39.403909Z digest=sha256:41f986474481f3beb0e652d26b7c341f447de97f98568ac910960bf02eaa6dc7

Observation 5b8937d3-e613-44c6-934d-f308ef9ba314 · outbound

This paper cites PubMedQA: A Dataset for Biomedical Research Question Answering.

OET: Optimization-based prompt injection Evaluation Toolkit PubMedQA: A Dataset for Biomedical Research Question Answering

Reference 13

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source=pdf_text observed=2026-08-16T04:38:39.412780Z digest=sha256:1c7588b076cf589dd5ea682ab61ec3e46e3777d8aeb10e93e8aa4d6ed0aa1af4

Observation 1c2185b1-cb18-406f-867a-31f9d347feec · outbound

This paper cites Challenges and Applications of Large Language Models.

OET: Optimization-based prompt injection Evaluation Toolkit Challenges and Applications of Large Language Models

Reference 15

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source=pdf_text observed=2026-08-16T04:38:39.428367Z digest=sha256:bcd0fdeceb30aa90cbda6bb9563751b874a6d49d152972fc91863586d2473e15

Observation 93ba8093-4ea3-4627-93e1-4a251d14ac48 · outbound

This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

OET: Optimization-based prompt injection Evaluation Toolkit Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 16

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source=pdf_text observed=2026-08-16T04:38:39.435643Z digest=sha256:f68ee60c1b0a9a9caee7aeb2eb04b65c8ed1b941c81cfd994845d65b7697ee1d

Observation f6b4a58d-65c1-493d-958c-8d4c6907811e · outbound

This paper cites Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks.

OET: Optimization-based prompt injection Evaluation Toolkit Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks

Reference 17

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source=pdf_text observed=2026-08-16T04:38:39.445465Z digest=sha256:09362cc797335e388f03fdbc0bc6977c956d43562d2364b4baf9500c37000c73

Observation 88bf41f4-09ff-4047-be4d-4c77132eef77 · outbound

This paper cites Jatmo: Prompt Injection Defense by Task-Specific Finetuning.

OET: Optimization-based prompt injection Evaluation Toolkit Jatmo: Prompt Injection Defense by Task-Specific Finetuning

Reference 18

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source=pdf_text observed=2026-08-16T04:38:39.454277Z digest=sha256:77cf92e923ad48074b592e5a74f9e0e53e9d8931c65aeb0d9f36e9e857d56fc1

Observation 4b864b20-42ff-4155-8efe-17e02ce98383 · outbound

This paper cites doi: 10.2196/58478.

OET: Optimization-based prompt injection Evaluation Toolkit doi: 10.2196/58478

Reference 20

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doi_truncated, observed 2026-08-16T04:38:39.761703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T04:38:39.466986Z digest=sha256:a55af4da6099a6bd8e986250c4254b7c1576c2d04d6244147cc6bd80719d4c32

Observation c6d96c42-e40a-4a5d-8c60-377f42a243d0 · outbound

This paper cites Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks.

OET: Optimization-based prompt injection Evaluation Toolkit Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

Reference 21

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source=pdf_text observed=2026-08-16T04:38:39.478848Z digest=sha256:6f2acdbb5a37468a90f755272b072c8d960bd5aab0302608619985e771002f05

Observation d70cbd30-f06f-4016-8cd8-9ac2f89aa61d · outbound

This paper cites Optimization-based Prompt Injection Attack to LLM-as-a-Judge.

OET: Optimization-based prompt injection Evaluation Toolkit Optimization-based Prompt Injection Attack to LLM-as-a-Judge

Reference 22

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source=pdf_text observed=2026-08-16T04:38:39.485121Z digest=sha256:2ecea5934a13dbce8c113330f0bfa2b202edd4112b72ee40c36e9268532a91e4

Observation c670c1e8-1771-4234-950e-564cd0c39fc5 · outbound

This paper cites an unresolved cited work.

OET: Optimization-based prompt injection Evaluation Toolkit Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-16T04:38:39.494803Z digest=sha256:4a06710003592e8f5170dcff049c2d3aab001b3f33b780bc4555f4bb5d1211e9

Observation 3286b078-3abc-452d-83c2-9b6aecf5dad5 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

OET: Optimization-based prompt injection Evaluation Toolkit LLaMA: Open and Efficient Foundation Language Models

Reference 24

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source=pdf_text observed=2026-08-16T04:38:39.500777Z digest=sha256:c1fca45a6b5550d3f8a17858ebf3150afdba15cab84e5f157393ae6c7329df22

Observation 201ee514-84cf-4300-9be3-01715e318968 · outbound

This paper cites Universal Adversarial Triggers for Attacking and Analyzing NLP.

OET: Optimization-based prompt injection Evaluation Toolkit Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:38:39.537808Z digest=sha256:9cfb83f830d3fa2287371d3d787fd34125105581fdd358195112d8ecd7dfb3c0

Observation cada5f55-9ebe-44ae-a3a8-28c3dd4facc8 · outbound

This paper cites FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks.

OET: Optimization-based prompt injection Evaluation Toolkit FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks

Reference 26

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source=pdf_text observed=2026-08-16T04:38:39.612688Z digest=sha256:54094b3387d36ff47afc41ceee0ac336c007d4f1304b19723260fee7773d948e

Observation fc447187-6cb3-4f18-ab66-ad417f98a79b · outbound

This paper cites Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

OET: Optimization-based prompt injection Evaluation Toolkit Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

Reference 27

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source=pdf_text observed=2026-08-16T04:38:39.648458Z digest=sha256:2b8197b209646beccaea7b8923c74b213d372bd8b0ac1b4a00ad0c4638dbcc09

Observation 5fbafc03-eb61-48c1-bfee-9862621488c4 · outbound

This paper cites Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models.

OET: Optimization-based prompt injection Evaluation Toolkit Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models

Reference 28

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source=pdf_text observed=2026-08-16T04:38:39.677783Z digest=sha256:0ec34cf1f78a002fb28df7d221563dbbd00ae5b978f32a69490e4586345d555e

Observation 053d531d-c8c9-478e-bc3b-a3cb9c4514c3 · outbound

This paper cites Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks.

OET: Optimization-based prompt injection Evaluation Toolkit Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 29

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source=pdf_text observed=2026-08-16T04:38:39.694018Z digest=sha256:93b7139855465c1fddfbe8da40a1fdcae59fc16e604793599e52ac0f4ecf137a

Observation faefc683-3cf1-49b0-b704-8c3122ee5b9a · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

OET: Optimization-based prompt injection Evaluation Toolkit Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 30

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source=pdf_text observed=2026-08-16T04:38:39.701686Z digest=sha256:00891c5d099e6f20d29398e85180e7c886b3bd6ce5eb135f1e003513c733ed9f

Observation bb12b6a1-beaa-4d2d-96bc-53e1708c53a6 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

OET: Optimization-based prompt injection Evaluation Toolkit SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 2016

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source=pdf_text observed=2026-08-16T04:38:39.461017Z digest=sha256:6a772a6e63690ee776631b6e4e3cb8111764fd2cf2346773b2874e313574d3ee

Observation 5dd833e8-7dd8-4637-9536-08c7a3e15998 · outbound

This paper cites doi: 10.18653/v1/P17-1147.

OET: Optimization-based prompt injection Evaluation Toolkit doi: 10.18653/v1/P17-1147

Reference 2017

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source=pdf_text observed=2026-08-16T04:38:39.419974Z digest=sha256:70f44b01145e5e162c940b7059bc607d708a0f63633e6a90eb3564007696a4af

Observation c39d7328-576e-4c46-bbf3-4caea2e1926c · outbound

This paper cites On Evaluating Adversarial Robustness.

OET: Optimization-based prompt injection Evaluation Toolkit On Evaluating Adversarial Robustness

Reference 2019

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source=pdf_text observed=2026-08-16T04:38:39.287874Z digest=sha256:cb42fb4d6a3a769b5ed9086acde7c7fbb11f6425525266e96fa02c8552c00bf1

Observation 7bdd038e-5a4f-4d5f-b4b6-f643a208d7ff · outbound

This paper cites FinQA: A Dataset of Numerical Reasoning over Financial Data.

OET: Optimization-based prompt injection Evaluation Toolkit FinQA: A Dataset of Numerical Reasoning over Financial Data

Reference 2021

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source=pdf_text observed=2026-08-16T04:38:39.307855Z digest=sha256:0dd5c99cbd6c3d8b66050f1086da7e961d70d079620f9784dfa9385d26b3cbfb

Observation ea5751c4-d577-4361-98a6-42b643dbda6d · outbound

This paper cites AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents.

OET: Optimization-based prompt injection Evaluation Toolkit AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

Reference 2023

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source=pdf_text observed=2026-08-16T04:38:39.315172Z digest=sha256:be257dbc2aafedb919de85a03ec9bb0de511e7fc92b36ba3081869d2dd12d8e1

Observation 1ec05193-af83-4686-b44e-c33464fa9278 · outbound

This paper cites AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs.

OET: Optimization-based prompt injection Evaluation Toolkit AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs

Reference 2024

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verified exact
local_arxiv, observed 2026-08-16T04:38:40.525286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T04:38:39.280879Z digest=sha256:2fa82689cd1e18714032dbd1bb56339a287482add6130cfc6f123487d17562eb

Observation 71080d02-5928-4413-afa1-2dedb0d76039 · outbound

This paper cites Get my drift? Catching LLM Task Drift with Activation Deltas.

OET: Optimization-based prompt injection Evaluation Toolkit Get my drift? Catching LLM Task Drift with Activation Deltas

Reference 2025

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source=pdf_text observed=2026-08-16T04:38:39.272991Z digest=sha256:b276266a7ec3eeefcd74f56df447e5d7c73758994ad33ad418008fb91a995364

Pith citing papers

Observation 533e6e12-6ca0-4dc5-a089-090fc9c83eed · inbound

Sentinel: SOTA model to protect against prompt injections cites this paper.

Sentinel: SOTA model to protect against prompt injections OET: Optimization-based prompt injection Evaluation Toolkit

Reference 33

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source=pdf_text observed=2026-08-07T10:30:14.284471Z digest=sha256:49abaed4872911278548341aec1134df9f57030aeac896ea7adfa385d0b10617

Observation 7f679763-cc3a-4c24-8125-049799287f05 · inbound

Assessing Automated Prompt Injection Attacks in Agentic Environments cites this paper.

Assessing Automated Prompt Injection Attacks in Agentic Environments OET: Optimization-based prompt injection Evaluation Toolkit

Reference 33

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arxiv_id, observed 2026-07-03T06:17:41.899803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-27T12:47:09.467463Z digest=sha256:06095dfcf2c23ea5c97eca0c84f2178f73c7c9e5dd16216518c5815d2d9a3c5e