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Paper Citation Record · LEDGER

Self-Guard: Empower the LLM to Safeguard Itself

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2310.15851.

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

pith.paper-citation-record.v1
2310.15851 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:12:04.857095Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T11:37:15.980886Z

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 11f6dce0-390e-43bc-a2c1-dccbb59c92c4 · 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 Self-Guard: Empower the LLM to Safeguard Itself

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T00:12:04.857095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T00:12:04.857095Z digest=sha256:56164ee7941f75ac9fb59808d5e13380e1c09ef3280de6260884ef838e7fdce1

Observation e84f6c0b-5284-4b79-bec1-c8d787b250b4 · inbound

Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation cites this paper.

Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation Self-Guard: Empower the LLM to Safeguard Itself

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-09T18:31:14.118069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:31:14.118069Z digest=sha256:eaceeb181fe1eed49eeab1a87a6e3904df460fdb36c1f05a73b1ed6151e34d8a

Observation 3bb0ae46-7edd-488f-a05a-11b2cfd86068 · inbound

Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models cites this paper.

Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models Self-Guard: Empower the LLM to Safeguard Itself

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:33.051132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:33.051132Z digest=sha256:3b093ab9c8bc22628980e8d901c33bea7b572f8b935f88f0e534783fd09aeb26

Observation 5b758c36-6a23-46fc-a2c8-3a6184d79f50 · inbound

ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction cites this paper.

ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction Self-Guard: Empower the LLM to Safeguard Itself

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:37:15.982305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-19T11:34:09.428653Z digest=sha256:ed748c3eaa7aeea3f92bfcc9a1ef4cd9ffc7e2859360c001c1f01279260b4740

Observation 714e8aaf-4dbc-4487-a8a7-24d05e0ffff2 · inbound

A Survey on Model Extraction Attacks and Defenses for Large Language Models cites this paper.

A Survey on Model Extraction Attacks and Defenses for Large Language Models Self-Guard: Empower the LLM to Safeguard Itself

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T22:23:13.547730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:23:13.547730Z digest=sha256:f8a185a43431b2f987ee0197611d3d266c1142f9c27a0116f1a3c0c4477a5d87