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Paper Citation Record · LEDGER

PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2402.15911.

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

pith.paper-citation-record.v1
2402.15911 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:03:01.160084Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:27:29.198098Z

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 ae4733b9-1679-4d0d-9a39-16e50b253d2f · inbound

Jailbreak Attacks and Defenses Against Large Language Models: A Survey cites this paper.

Jailbreak Attacks and Defenses Against Large Language Models: A Survey PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:20:44.773775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-15T02:20:44.368219Z digest=sha256:f981afe4f2704e8b2397c2a4a88b23b446431181b8172d15e0864fef16f61560

Observation 2eda4dab-6848-453a-84ce-1c5af30e8cec · inbound

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents cites this paper.

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-14T01:35:51.237222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-14T01:35:50.992477Z digest=sha256:f92b86ec6bf35c511cf94848e80888970f1f508a936e90f8b710351ec4174b10

Observation 6bbfa8a2-5f24-4183-ad8b-9dd88745baac · inbound

Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment cites this paper.

Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T11:03:01.160084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:03:01.160084Z digest=sha256:fd7584a6f21db7ecc9c77f8c2d7916e9e853df9413f47ba12217d23cdcb6f084

Observation 915f8aba-f70c-485a-ab68-f50a5f284d90 · inbound

LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds cites this paper.

LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T20:53:37.615066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:53:37.615066Z digest=sha256:0b8dce77eb7d50290a4675f41fc9a9038c12d7455305650d9e8d28beef1cb337

Observation 17ff1f8b-e15b-43da-9b80-9da31bfc2cb6 · inbound

Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models cites this paper.

Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T00:12:04.983389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T00:12:04.983389Z digest=sha256:167cbf82c9c3777aa684ad74b9bb783a99a05388dfabab3ebc17710a064e262b

Observation 2c41d5a4-730b-4c3a-8a68-191552b8341d · inbound

Agents Are All You Need for LLM Unlearning cites this paper.

Agents Are All You Need for LLM Unlearning PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-09T19:14:53.691478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T19:14:53.691478Z digest=sha256:803533258318bd04d915026794e706dd50b5e2c8284b39ea151ddb12409cb68d

Observation fd5542b4-8da6-4dbc-a958-44f704face51 · inbound

Position: Adversarial ML for LLMs Is Not Making Any Progress cites this paper.

Position: Adversarial ML for LLMs Is Not Making Any Progress PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-09T12:47:21.714790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T12:47:21.714790Z digest=sha256:e96bab6e5d18cce81504f418f9407d0ccbc81d9e212240e7ab280bc5896683e2

Observation 10498fd5-ae2b-48cf-9c57-cbc237cc3147 · inbound

FORTRESS: Frontier Risk Evaluation for National Security and Public Safety cites this paper.

FORTRESS: Frontier Risk Evaluation for National Security and Public Safety PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T00:15:01.975098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:15:01.975098Z digest=sha256:28123a812ce927e2d751bd40f3f0f8636002879c7adfc341392853640e28a13f

Observation c8da5bc0-1678-4e7d-a684-7202620ad8c0 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T20:31:33.306997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:31:33.306997Z digest=sha256:08d895c0eb53bdf4cb0d9e7c010929aa3444e106d2592664c4095e0e174aad67

Observation 5a803722-075c-4ca4-9f3a-14fe95ff78e9 · inbound

Agent Security is a Systems Problem cites this paper.

Agent Security is a Systems Problem PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-20T09:03:09.830565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-20T09:02:59.719090Z digest=sha256:7c90ae436a2710bef9f4ee7296f6b0d4e83cc8a49db1f3e3b035ecd6b43be3bc

Observation 7a9a73d9-29d0-4f53-bfb5-d8480f1248f7 · inbound

Agent Security is a Systems Problem cites this paper.

Agent Security is a Systems Problem PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-21T07:44:02.881062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-21T07:43:14.250188Z digest=sha256:617b53e21d39e9475f3d4d6248174164a21cdc0eebacbe0a621feac747c2a6d7

Observation af6594d7-cf8c-4d3f-8284-6f6b4cbe31d1 · 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 PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Reference 55

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T00:27:29.199712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-27T17:15:51.375580Z digest=sha256:f14b17b90a716a839366e0836e02ddbc77b23ef09e90119bd9aed3e7114ed8df