Paper Citation Record · LEDGER

Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2409.07353.

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

pith.paper-citation-record.v1
2409.07353 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:58:53.771720Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:06:27.571754Z

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 3d35a05d-07f3-46a5-bd3a-e62d447aa48e · inbound

Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models cites this paper.

Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:53.771720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d39fb8f3-1e45-4dc2-aee4-bd653cc12e58 · inbound

When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs cites this paper.

When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

Reference 114

Resolution: unresolved; no resolver link, observed 2026-08-08T15:38:17.510011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec94ade7-be04-4118-b8bf-7077c790eece · inbound

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations cites this paper.

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

Reference 152

Resolution: unresolved; no resolver link, observed 2026-08-07T19:45:19.994347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b762686-c1fb-4fde-b376-457f71a46112 · inbound

Diffusion-based Cumulative Adversarial Purification for Vision Language Models cites this paper.

Diffusion-based Cumulative Adversarial Purification for Vision Language Models Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:16.106884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40a1995e-ac82-4e79-8dfe-e86f37f1c55f · inbound

Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding cites this paper.

Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T11:54:42.969519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed725f78-e908-409c-a281-24b41f66e55b · inbound

Investigating Adversarial Robustness of Multi-modal Large Language Models cites this paper.

Investigating Adversarial Robustness of Multi-modal Large Language Models Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:06:27.573871Z

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.