Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2401.17263.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:38:39.694018Z
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 2026-08-22T06:32:14.747728+00:00.
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 2026-08-22T06:32:14.747728+00:00.
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 2026-08-22T06:32:14.747728+00:00.
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 2026-08-22T06:32:14.747728+00:00.
Observation 6718864d-b211-4762-a6eb-a7ebbeb791ac · inbound
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models cites this paper.
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:25:04.916403Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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.
Observation 053d531d-c8c9-478e-bc3b-a3cb9c4514c3 · inbound
OET: Optimization-based prompt injection Evaluation Toolkit cites this paper.
OET: Optimization-based prompt injection Evaluation Toolkit Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:38:39.694018Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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.
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 2026-08-22T06:32:14.747728+00:00.
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.
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 2026-08-22T06:32:14.747728+00:00.
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 2026-08-22T06:32:14.747728+00:00.
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 2026-08-22T06:32:14.747728+00:00.