Paper Citation Record · LEDGER

Universal Adversarial Triggers for Attacking and Analyzing NLP

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:1908.07125.

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

pith.paper-citation-record.v1
1908.07125 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:17:01.699125Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 645a6d85-5ddb-43fd-b1f8-0d801cce792c · inbound

Deduplicating Training Data Makes Language Models Better cites this paper.

Deduplicating Training Data Makes Language Models Better Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-24T13:39:31.665485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 51751e4c-a6b8-468f-8be4-42ff42147cec · inbound

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

Universal and Transferable Adversarial Attacks on Aligned Language Models Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-24T07:44:08.501178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d5deb967-4af0-4aae-ba2f-2045e62dfd03 · inbound

Universal Adversarial Attack on Aligned Multimodal LLMs cites this paper.

Universal Adversarial Attack on Aligned Multimodal LLMs Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T11:17:01.699125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 546d9cc7-131f-4da2-9f4f-7e7b30a05d13 · inbound

Robust and Efficient AI-Based Attack Recovery in Autonomous Drones cites this paper.

Robust and Efficient AI-Based Attack Recovery in Autonomous Drones Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:31:23.552833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b319180-7c6c-4567-8f10-b5ce34044201 · inbound

Lifelong Safety Alignment for Language Models cites this paper.

Lifelong Safety Alignment for Language Models Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:00:08.447260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b60321bc-507e-4225-b546-64b0f2acdad9 · inbound

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models cites this paper.

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 160

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:48.604253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45024b38-fe8e-4cd8-a652-cb0dbcefcca1 · inbound

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations cites this paper.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:23:40.132422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29f60ec0-f4e9-486d-9abe-6d832cf5d7bb · inbound

VERA: Variational Inference Framework for Jailbreaking Large Language Models cites this paper.

VERA: Variational Inference Framework for Jailbreaking Large Language Models Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:07:05.264755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 923ae7d5-0634-43e6-9a9a-5af5f2a7d9bd · 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 Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:48.488605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 816c07a6-52ba-454e-b51d-8b1b1d560c77 · inbound

Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree cites this paper.

Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T15:52:56.831143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24711129-aa92-49be-a2e3-33519b8d36b1 · inbound

Augmented Vision-Language Models: A Systematic Review cites this paper.

Augmented Vision-Language Models: A Systematic Review Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 113

Resolution: unresolved; no resolver link, observed 2026-08-06T14:33:42.557179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dfa5b0f-5e9b-454c-911e-58edb954df70 · inbound

BarrierSteer: LLM Safety via Learning Barrier Steering cites this paper.

BarrierSteer: LLM Safety via Learning Barrier Steering Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:05:26.804202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc96eaf6-a4e4-4f9d-ba4c-297631667e18 · inbound

Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers cites this paper.

Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-10T21:55:49.767304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2762a09e-26de-49fb-a6a3-ef5784918d2a · inbound

Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates cites this paper.

Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:55:31.820741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8eedd4cf-34dd-431f-855d-cfa4c1fa16b2 · inbound

Online Learning-to-Defer with Varying Experts cites this paper.

Online Learning-to-Defer with Varying Experts Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:07:13.136968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c4f2aea-6f1c-48aa-a3a0-8b86bfbd90b1 · inbound

Online Learning-to-Defer with Varying Experts cites this paper.

Online Learning-to-Defer with Varying Experts Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:09:51.360290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db9f0958-618b-46a4-a827-74c80e0819f2 · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 205

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:17:54.278529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02f8a46d-bd58-4d81-99ba-2c869bb3b592 · inbound

ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation cites this paper.

ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:04:58.854227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e26aab9f-c653-43bd-9514-be1e631b06f8 · inbound

Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization cites this paper.

Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:23:50.902580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0fc62c5d-cb4a-4a4a-ade8-9e27ecc31e77 · inbound

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study cites this paper.

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:18:03.177073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db2b6f8e-06ef-4f6c-80a5-b82315db24f3 · inbound

Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing cites this paper.

Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T10:33:33.025490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eac7930-200b-4736-9c78-1bedc3f74f8b · inbound

Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing cites this paper.

Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 641

Resolution: unresolved; no resolver link, observed 2026-08-02T09:39:08.416040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.