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Paper Citation Record · LEDGER

Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2401.17263.

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

pith.paper-citation-record.v1
2401.17263 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:29:50.195132Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T12:45:44.896570Z

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 99b24150-1a03-4ac5-9292-e69d1235d680 · inbound

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

Jailbreaking Black Box Large Language Models in Twenty Queries Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:48:33.327484Z

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.

source=pdf_text observed=2026-05-12T09:48:31.721745Z digest=sha256:b271b3ebf14d1e7bd82432d470a6115d9a2811aad236a0b2f3148f3f9e954b0a

Observation 726cd05c-10cf-47d5-b5b6-234880d752ae · inbound

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models cites this paper.

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 115

Resolution
verified exact
arxiv_id, observed 2026-05-13T13:43:11.175792Z

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.

source=pdf_text observed=2026-05-13T13:43:11.024069Z digest=sha256:e2517190d2a4f20aa01fc363a8c7235fa149b91a70ca1df8123a8beb69732bd9

Observation 83210014-3757-4040-b399-720360c5d0be · inbound

Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI cites this paper.

Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-24T02:48:47.341043Z

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.

source=pdf_text observed=2026-05-24T02:48:10.934475Z digest=sha256:726c18b5ee55567cd409c3add4e7597df8f76dd4cd634e09b10a937606fe4249

Observation 6b540e4d-dbc5-4bb3-bb7f-fe88a09f5578 · inbound

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

Jailbreak Attacks and Defenses Against Large Language Models: A Survey Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 121

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:20:44.517990Z

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.

source=pdf_text observed=2026-05-15T02:20:44.368219Z digest=sha256:ba9390360bc13fe75b3bcae280f6118eec672a1827209d6a33cc74f712484fbd

Observation be87d481-dcf7-4fbc-b8cd-485ef85dbc5d · inbound

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives cites this paper.

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:50.195132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:29:50.195132Z digest=sha256:d55df6bef28ac3bf5eaaba37901277c3e1b9570208a4c1530b051ca3ccc25d24

Observation b3e86dd3-3943-422a-8225-1743929d5e3a · inbound

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning cites this paper.

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 199

Resolution
unresolved
no resolver link, observed 2026-08-06T04:47:25.685053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:47:25.685053Z digest=sha256:d79ce7f3cbd29761593bcd424a2793ca81e1c5c91a8bd9c76419a8d12cbda878

Observation c141b216-a031-4b87-b6b4-07cf68b46eed · inbound

ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments cites this paper.

ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-19T01:02:54.687751Z

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.

source=pdf_text observed=2026-05-19T01:02:07.088724Z digest=sha256:cd34280cc600537c820d04b59f8978a679df07619c0dad5ad90c3af962b157ca

Observation cadf9baa-dbf5-470b-94e7-9452b8d9df02 · inbound

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection cites this paper.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T22:24:23.388868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.388868Z digest=sha256:15ef4d603354ea4fc12f4ff0d8ecb4ff2f98f0b63e7ae334799d8315fd652f7d

Observation 4e06eab7-6159-4db0-bf0c-3751c1185b78 · inbound

Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs cites this paper.

Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-05-21T19:00:30.330609Z

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.

source=pdf_text observed=2026-05-21T18:58:53.183734Z digest=sha256:dafdf1c4b8287508a557ec4ba3d1fc5f63dfb299794979372f39894faa2f6b5d

Observation a854484c-64cc-4376-b63d-9fb68d732f7f · inbound

Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense cites this paper.

Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-07-01T12:45:44.898063Z

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.

source=arxiv_source observed=2026-07-01T01:44:07.700127Z digest=sha256:4520fe9d1ff9de3b01a79c9a883579e5577ef51d24212c5a5b2666fad46d7c12

Observation ba88e58f-69c8-4f31-95aa-c3b55c7e41b4 · inbound

Addressing Over-Refusal in LLMs with Competing Rewards cites this paper.

Addressing Over-Refusal in LLMs with Competing Rewards Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-07-01T08:55:35.592963Z

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.

source=arxiv_source observed=2026-07-01T06:59:12.695984Z digest=sha256:19c2dffaf18bb8d3e7c89720d332f61804d88d5d7cfaa9e9e7e0a29b1e2e3ab9