Paper Citation Record · LEDGER

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2005.05909.

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

pith.paper-citation-record.v1
2005.05909 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 31 of 31 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 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:59:52.396711Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:08:08.786365Z

Reference resolution

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verified exact0
verified fuzzy0
unresolved0
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External citation measurements

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Outbound references

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Pith citing papers

Observation 8ad5dda9-6ee8-4b66-a6b3-5ea4c1bd3df3 · inbound

Baseline Defenses for Adversarial Attacks Against Aligned Language Models cites this paper.

Baseline Defenses for Adversarial Attacks Against Aligned Language Models TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T23:24:40.176671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 37ff3ae8-ba55-43cf-93a6-bd49b1340fee · inbound

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks cites this paper.

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T17:11:00.925500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0e26e03a-06b2-4682-b32d-8c88a990b433 · inbound

Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet? cites this paper.

Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet? TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T19:37:58.422888Z

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Observation d663a512-83b9-41fb-9a5b-7f0009bd9ca7 · inbound

Adversarial Attacks on Hyperbolic Networks cites this paper.

Adversarial Attacks on Hyperbolic Networks TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T04:20:52.715422Z

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Observation 8a7421a1-4cfa-4828-a159-2125bc2ae16e · inbound

Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks cites this paper.

Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T23:10:26.157719Z

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Observation 16c52e12-2b80-400c-849e-4ade236a231b · inbound

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting cites this paper.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.028265Z

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Observation a4b67705-c050-4d08-af0b-1b947052ebab · inbound

Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages cites this paper.

Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T15:40:53.177612Z

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Observation 85cddb54-5b88-467a-8350-483f582ab7fb · inbound

Towards Action Hijacking of Large Language Model-based Agent cites this paper.

Towards Action Hijacking of Large Language Model-based Agent TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T15:41:57.293134Z

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Observation 11439ee7-65cf-4088-ab14-257f9a529a17 · inbound

SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs cites this paper.

SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:34.494112Z

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Observation ccc52258-f62b-4fa7-84cd-293a857cff19 · inbound

Confidence Elicitation: A New Attack Vector for Large Language Models cites this paper.

Confidence Elicitation: A New Attack Vector for Large Language Models TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T22:06:38.935952Z

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Observation d19593e3-947a-4c98-8f36-c4506b95ca03 · inbound

Decoupled Global-Local Alignment for Improving Compositional Understanding cites this paper.

Decoupled Global-Local Alignment for Improving Compositional Understanding TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T10:59:52.396711Z

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Observation da6430d9-597b-4cef-af04-018df6dc7457 · inbound

Statistical Runtime Verification for LLMs via Robustness Estimation cites this paper.

Statistical Runtime Verification for LLMs via Robustness Estimation TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T10:36:45.998864Z

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Observation 8f6847ba-bca2-4b42-86d5-5839ccfc19f2 · inbound

Improving Routing in Sparse Mixture of Experts with Graph of Tokens cites this paper.

Improving Routing in Sparse Mixture of Experts with Graph of Tokens TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T04:41:49.390654Z

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Observation 3d23c6e0-dd3b-4975-9538-84b8fd3bba74 · inbound

SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models cites this paper.

SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:19.444402Z

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Observation f19981b6-7ddc-49af-af5d-ccb9b827bb9b · inbound

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings cites this paper.

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:21.396314Z

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Observation 6ad48054-2851-4434-8254-b444e1fc6a33 · inbound

A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks cites this paper.

A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:21:35.071014Z

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Observation cc92c8af-298a-4a08-9377-4d512610e210 · inbound

Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models cites this paper.

Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T22:59:48.055868Z

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Observation 8e07427f-0d7b-4129-ba89-fc6fc81c6cf5 · inbound

The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy cites this paper.

The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T21:53:17.358520Z

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Observation d12f18d4-36e4-477e-96fd-5040d68c2f01 · inbound

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level cites this paper.

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:54.832482Z

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Observation 103ae790-ec3e-4edf-bf4e-122cf26e8719 · inbound

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training cites this paper.

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:48.347629Z

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Observation 90d3897a-c5a1-4268-b98c-e8fea1bffea5 · inbound

ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model cites this paper.

ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T15:46:44.823362Z

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Observation 7a4597ce-244e-4700-bc9c-8f6667df236f · inbound

Embeddings to Diagnosis: Latent Fragility under Agentic Perturbations in Clinical LLMs cites this paper.

Embeddings to Diagnosis: Latent Fragility under Agentic Perturbations in Clinical LLMs TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T17:50:55.719057Z

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Unavailable: canonical work link unavailable.

Observation 28e3c9ee-2ecf-4ca4-a0bf-7e76b20cf39e · inbound

Practical, Generalizable and Robust Backdoor Attacks on Text-to-Image Diffusion Models cites this paper.

Practical, Generalizable and Robust Backdoor Attacks on Text-to-Image Diffusion Models TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:12.214353Z

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Observation 56507a9b-f66c-4a67-98de-551eaa62ab92 · inbound

Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution cites this paper.

Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:01:51.970128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 442df1a7-ddfc-4754-b63c-9e3349473728 · inbound

Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution cites this paper.

Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T18:51:05.174579Z

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Observation 8f6bdc07-69e0-494a-9313-7341cd0a7d43 · inbound

SALMAN: Stability Analysis of Language Models Through the Maps Between Graph-based Manifolds cites this paper.

SALMAN: Stability Analysis of Language Models Through the Maps Between Graph-based Manifolds TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T17:14:37.108958Z

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Observation f7e92ad1-029a-45d4-b334-2615d2261f15 · inbound

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research cites this paper.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.527887Z

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Observation 5400241e-2231-40f9-a9f7-acb2d0a48260 · inbound

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges cites this paper.

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-06-28T18:42:29.237410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d51d2cfd-145a-4336-9c8f-a180870035c0 · inbound

AI Researchers Must Help Lead Arms Control to Mitigate Military AI Risks cites this paper.

AI Researchers Must Help Lead Arms Control to Mitigate Military AI Risks TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:08:08.787613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2ae21208-d47e-4b57-acfb-f091325ba0d1 · inbound

Evaluation of Adversarial Robustness in Arabic Language Models cites this paper.

Evaluation of Adversarial Robustness in Arabic Language Models TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T01:26:17.518510Z

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Observation 52d01433-889e-4799-bfd2-ff7cf28c2e68 · inbound

Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical vs. Transformer-Based Detection Systems cites this paper.

Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical vs. Transformer-Based Detection Systems TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:19:22.176307Z

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Unavailable: canonical work link unavailable.