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Paper Citation Record · LEDGER

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification

As of 18 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2504.13562.

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

pith.paper-citation-record.v1
2504.13562 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:10:31.131754Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved49
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 62ae4d59-dd8c-457d-8afb-1fa5b705cfbc · outbound

This paper cites Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:30.898210Z digest=sha256:b6ccb1a697131b0f4eb0b8999ea285e68efd56320f2b45241d5821ef41d7bebc

Observation d58959d5-2197-41e6-8d01-c45298f405c0 · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 2

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no resolver link, observed 2026-08-16T12:10:30.904167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:30.904167Z digest=sha256:5bda449367edaf8641e6404f6ab8f7a454d3d89b059690e320245f7816c085f5

Observation 2ede2e4a-7d9d-4021-8cc7-cc0ba662f677 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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no resolver link, observed 2026-08-16T12:10:30.910396Z

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source=arxiv_source observed=2026-08-16T12:10:30.910396Z digest=sha256:e4a43bbdb14779559fb3a06e53d669ebd8834eea0213a95b38ccc21b2eba02b0

Observation 245933f6-3bad-4a58-866a-850cf9a12834 · outbound

This paper cites Bender, Timnit Gebru, Angelina McMillan - Major, and Shmargaret Shmitchell.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Bender, Timnit Gebru, Angelina McMillan - Major, and Shmargaret Shmitchell

Reference 4

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no resolver link, observed 2026-08-16T12:10:30.915634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:30.915634Z digest=sha256:87cff7ae90679deeb15192d5c56b95468a6a1bc978263ca35369c33a18e69622

Observation 18e1e37a-e73a-4df6-92d2-fade10287c68 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-16T12:10:31.959640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:30.920517Z digest=sha256:7a61a21b24e0b69b09d4a244e0ccc20e3c6cb5a9148cbbb0ee75a7c503f5914f

Observation b600ed47-243c-4441-adb5-d84d93d8382d · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 6

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no resolver link, observed 2026-08-16T12:10:30.925082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:30.925082Z digest=sha256:d5d3febe6b261a94b4cfa28c6f9099be4729c1699f76d3c96f8488cec71c543a

Observation fffb375a-25d3-45e4-a074-0cfa3666257f · outbound

This paper cites JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 7

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no resolver link, observed 2026-08-16T12:10:30.930303Z

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source=arxiv_source observed=2026-08-16T12:10:30.930303Z digest=sha256:06eac5ffc67a5dba4e99555b47bc0d9728d4aee5f66bbee68b5210d08675a83b

Observation 7fd13752-c144-4f96-9926-123a15e39aa2 · outbound

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

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 8

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source=arxiv_source observed=2026-08-16T12:10:30.935163Z digest=sha256:92ff39dc70ff3b8ef218652049bd631e737f492431e062bdffe997e63479d424

Observation 5f908982-cb68-417b-9abc-8fb5f5cf95d9 · outbound

This paper cites Injecting Universal Jailbreak Backdoors into LLMs in Minutes.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Injecting Universal Jailbreak Backdoors into LLMs in Minutes

Reference 9

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source=arxiv_source observed=2026-08-16T12:10:30.939879Z digest=sha256:fbb4aeb639c2a3a3950d3b4a739b2f1b686b8917faf535e54e1411c2c1944dd4

Observation 3ca86a27-66a4-4eb5-b4bb-a387c53d98ba · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 10

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:30.944718Z digest=sha256:671914578e4881d724223b398291f867fe7fabdee72ecee1b6a0a283ecd4d765

Observation a90b09ef-8ac4-4e71-876e-b7d460e28410 · outbound

This paper cites OR-Bench: An Over-Refusal Benchmark for Large Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification OR-Bench: An Over-Refusal Benchmark for Large Language Models

Reference 11

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source=arxiv_source observed=2026-08-16T12:10:30.948693Z digest=sha256:72519da2fc2d8d60b9445c3829d4dde62fe9b2ead0d94d0b9f451cbe23965cea

Observation b00dda28-cf7a-4a73-a981-9336b9192a41 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 12

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raw_fallback, observed 2026-08-16T12:10:31.935707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:30.952547Z digest=sha256:2e4d742a508c40b0ad6cdd04848caf8535158c4751869821f1e3fcb9f2a7325c

Observation f625397d-fc9d-4e2d-9788-082c094a3b0e · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-08-16T12:10:30.956078Z digest=sha256:aa7f931eff1ad7caea458aa55fbca4447096d3d1f67153e37077a7e56436484b

Observation 77d3d7ce-ea34-46aa-b1fb-04b9522bfce0 · outbound

This paper cites Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing

Reference 14

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source=arxiv_source observed=2026-08-16T12:10:30.960158Z digest=sha256:caff1bd6fc39056ed6eb0d92f0f30165e3630b552556ff7da4125958a893d8ce

Observation a1389b44-281d-4d50-b791-e7350b403733 · outbound

This paper cites Mixtral of Experts.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Mixtral of Experts

Reference 15

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no resolver link, observed 2026-08-16T12:10:30.964242Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:30.964242Z digest=sha256:683c183c229c298132ed891c6ba6d331998938d9e343b87289eeeb1c5fa129ba

Observation aac9a438-846e-4cff-9e05-9a54cf1bcfb3 · outbound

This paper cites WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models

Reference 16

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source=arxiv_source observed=2026-08-16T12:10:30.968917Z digest=sha256:7bf3a7e5bd3ffa74a9c3e48d9159448a6d7ed276cf09437e0845dc0259a8ca06

Observation 0fb49374-8d37-42f5-a66b-36c1df2b2d4d · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 17

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raw_fallback, observed 2026-08-16T12:10:31.922004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:30.974149Z digest=sha256:bdd829469a77cf3ca5ae1a279988f9be75e1dc3c0ba9e9c600f67506178bfcef

Observation 6b46e0dc-c6ca-4f1e-9976-5d8a59afcfcd · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 18

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no resolver link, observed 2026-08-16T12:10:30.978703Z

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source=arxiv_source observed=2026-08-16T12:10:30.978703Z digest=sha256:0d7c23fd1c395dee490949dd00d8e478f83c9d3bc724b3f69b5290a9dc53cb65

Observation 3493a01b-a5f8-40d1-8da2-7247ba2ac36c · outbound

This paper cites DeepInception: Hypnotize Large Language Model to Be Jailbreaker.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification DeepInception: Hypnotize Large Language Model to Be Jailbreaker

Reference 19

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source=arxiv_source observed=2026-08-16T12:10:30.983352Z digest=sha256:33317503d76d1927bc2a8fbfbd34b3ad3e04c29d8c16d810c493e6b0c07cae38

Observation 25ba5215-cad9-43cf-9589-6974ce7b167b · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 20

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raw_fallback, observed 2026-08-16T12:10:31.907722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:30.988165Z digest=sha256:1e2f6ae6b153e1d48c7db17d2266025ade8642e155d1d0ddc61201eda21d6ba1

Observation a3965006-66f1-4af7-b424-54cc5c34482a · outbound

This paper cites Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs

Reference 21

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source=arxiv_source observed=2026-08-16T12:10:30.992805Z digest=sha256:bb17d3f8eac91c381f86a3f31cad9e49dd69b569fc1f555f0c8d3842f7901b0e

Observation 9402466e-5bd3-43c8-ad65-98a5b4b713f9 · outbound

This paper cites AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 22

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no resolver link, observed 2026-08-16T12:10:30.997511Z

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source=arxiv_source observed=2026-08-16T12:10:30.997511Z digest=sha256:1819ef8ec2426af1e62788856745c47eff6adc9b5241bb26a12a3228a5273184

Observation 0dba8af0-03c4-4526-a6d5-e5399af47a86 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 23

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raw_fallback, observed 2026-08-16T12:10:31.892305Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.003090Z digest=sha256:6da298b5001a20936693f8cfbeecef2436a0a16f9ee2bde7d5ad52e2f76f9812

Observation cca35e4b-f552-4d59-99dc-e326b1bf4058 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 24

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raw_fallback, observed 2026-08-16T12:10:31.875915Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.008312Z digest=sha256:1d29bad9a55d124539ee6d553a748801dc036b8af59de4d9f615e8f678e3e659

Observation 09df1fb0-8d16-476d-bdac-33c129394e40 · outbound

This paper cites GPT-4 Technical Report.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification GPT-4 Technical Report

Reference 25

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source=arxiv_source observed=2026-08-16T12:10:31.013275Z digest=sha256:6716f5f61a3b1b7a38d90bc685edaedd85329300a1873c8b5d36985a28af1017

Observation 77fca6da-6759-4b32-a6e5-4e44c4cc73df · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 26

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raw_fallback, observed 2026-08-16T12:10:31.860430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.017732Z digest=sha256:0862e89d3ba06d7d8ffc26c5d93d36b34c30696c6dd67059af60d65ce41e8837

Observation 91c07911-987f-4f36-af22-692348e74303 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 27

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source=arxiv_source observed=2026-08-16T12:10:31.022326Z digest=sha256:a0f2051f5144bc991ef37d7bf21ced6a0784315d85c43840bfdc77af274c9578

Observation 28163107-d7c6-4216-b8a1-b3b2d3f08e8b · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 28

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no resolver link, observed 2026-08-16T12:10:31.026735Z

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source=arxiv_source observed=2026-08-16T12:10:31.026735Z digest=sha256:8346b857d054407e510339517c48b94cb0c49eddae2d8c4bf2a954c1b056a1a7

Observation 22002250-cb8b-45a8-88bf-b3ecec84cc53 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 29

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source=arxiv_source observed=2026-08-16T12:10:31.031366Z digest=sha256:4361955d5a90facb70429d98f7894ad1989dfd10ddb283d7308065825123dc8e

Observation 0ce3cdde-fb4f-4623-922d-eba00cc09e96 · outbound

This paper cites do anything now.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification do anything now

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:31.845971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.036107Z digest=sha256:960bad70b9c23ba0acc9bbbe9df1eab78ee465ce23a8a592b48880f1b4be5676

Observation c84cdb34-fce2-45c7-afff-22cb6dacb3af · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 31

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source=arxiv_source observed=2026-08-16T12:10:31.040694Z digest=sha256:bea761e321d94db35735cab9c6340651d743e48b05e24a52a02a51f65c2f0cbf

Observation fbfe326f-9e8d-4b9f-8e54-c1bbbfbd1e94 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 32

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no resolver link, observed 2026-08-16T12:10:31.046231Z

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source=arxiv_source observed=2026-08-16T12:10:31.046231Z digest=sha256:7c701a1ed5f8fe653a4fc576da13ece49d235255f9569a56e5abe03ed18ef41c

Observation 0c50aca7-63f7-4a47-b6ce-969c24fcdf55 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 33

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no resolver link, observed 2026-08-16T12:10:31.050526Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.050526Z digest=sha256:ea93fc12241d11038f11d79302e512e4db8298ec4f3201cb1f3e641beec871d7

Observation a3a3f5d5-f0c5-4208-a783-e54ec33b01d8 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 34

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raw_fallback, observed 2026-08-16T12:10:31.830997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.055141Z digest=sha256:d89ef1902f98c15d21aa9af38f09b8a561ba7ff31ffafa9a48466bdbbb4458ad

Observation ad9db7de-fa27-496f-bae5-2d5b87b42f9c · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 35

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unresolved
raw_fallback, observed 2026-08-16T12:10:31.815248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.059987Z digest=sha256:2efcfa04046944a7fc7b40349e4a0b7f8b5110b672bdeaae3aee86b883e0108e

Observation c3d0bbf9-03d8-49cd-8153-bb7f999c5e68 · outbound

This paper cites Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations

Reference 36

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no resolver link, observed 2026-08-16T12:10:31.064804Z

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source=arxiv_source observed=2026-08-16T12:10:31.064804Z digest=sha256:4d33f5ed09162c5a3238409aa0e3527a3ceef7e879366d90d40c7dbc7a77b408

Observation b454e88e-88f5-494e-bbc2-cc822f24a2ef · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 37

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unresolved
no resolver link, observed 2026-08-16T12:10:31.069229Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.069229Z digest=sha256:1c18c0fd64353db4b303841bb10854ddfdce7702774aae5c6fa95440b3ecd491

Observation f809632a-e5c1-4ace-94e5-516b24aafc35 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-16T12:10:31.791377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.073293Z digest=sha256:389e827ea26b3e0e8ff851c036786115ac96b0a786090cf5c10bcf2b564eef1c

Observation 5a701a33-ae22-4dda-9ab4-a90d4ad77df7 · outbound

This paper cites A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.077809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.077809Z digest=sha256:b2828dae927e987f45c9ede1120854440e5db39ca0aff3b2a0565d27a1ec6399

Observation 4fbc9df5-e981-4a39-b09a-557393605066 · outbound

This paper cites GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.083034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.083034Z digest=sha256:95d8852fb4f624683b061304f01c7f9f5fe8da456988d342c91195582dc7e6ea

Observation 5fa10b5e-15c8-4546-b981-9aa37f58c252 · outbound

This paper cites Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.088250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.088250Z digest=sha256:e2a18ef665c9fda71f3725aa1ce287c2998186eb93d45b742a4eb1a6bf2c0808

Observation e0e1ef83-954b-4e3d-baf3-6fe8801ae300 · outbound

This paper cites AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.093405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.093405Z digest=sha256:76ba73fa2f7a754d54a82f95357d48f3b1b40b35dabc01600461689b2a698027

Observation 7df04aee-21bb-46bd-9156-c2910edfe740 · outbound

This paper cites From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.098839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.098839Z digest=sha256:f809375fc85726425751b59222b916edb5a4992008169e98edf6d88f4b560593

Observation a167b720-56ec-45c0-b909-80cdf83c8234 · outbound

This paper cites an unresolved cited work.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-16T12:10:31.776451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T12:10:31.103761Z digest=sha256:2c0fe0bebd49b02774ccb59c1649b09ce47cbbde120cab81c3026852f6a6dac2

Observation f68c0b31-1ae0-4ea4-a11b-c12c54df7d38 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Instruction-Following Evaluation for Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.108393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.108393Z digest=sha256:9e9a6c6d7f85d209c941d2d270d215fc820fe456daa8b91bbd919525b57bc03f

Observation 6c8fdd32-ceb5-46cb-98db-96a416576729 · outbound

This paper cites EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.112869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.112869Z digest=sha256:8ba27c192ffc78403ae65f42ec81e544d82da93e3446c885451f835609616b4c

Observation 0a81a26c-05cf-4e47-ab71-dfbc0f7b0720 · outbound

This paper cites Don't Say No: Jailbreaking LLM by Suppressing Refusal.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Don't Say No: Jailbreaking LLM by Suppressing Refusal

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.116961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.116961Z digest=sha256:b07ddd814a35cbfd4adfb9becd5fe40907cc2a7c32e070163e99189598cbea6c

Observation 5e069761-60d8-472b-acb5-1e6bbb1abf55 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.122045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.122045Z digest=sha256:77a9b83fca29846821abdda09351bf1209af726c3bc5a4fe92aa95b6eba22672

Observation e724aed9-0c47-4b6e-bdbe-b1b18cf33114 · outbound

This paper cites online" 'onlinestring :=.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification online" 'onlinestring :=

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.126712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.126712Z digest=sha256:51ae2d0b1c9b947b94de7c366b375c731b2f15212b44bb640eae0da94622f76a

Observation cc4eada2-5e12-4294-ab6c-4d89b87174ba · outbound

This paper cites write newline.

DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification write newline

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:31.131754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:10:31.131754Z digest=sha256:367ebd28081246190913ba04a99d64d76706a64945b48f3df41aa29abfd964d0

Pith citing papers

No inbound Pith citation observations are available.