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Paper Citation Record · LEDGER

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs

As of 11 August 2026, this Paper Citation Record lists 100 of 165 outbound references and 1 inbound Pith citation observation for arXiv:2412.20087.

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

pith.paper-citation-record.v1
2412.20087 v1

Coverage vector

measured 100 of 165 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:40:18.992038Z

measured 101 of 101 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-15T13:15:31.225992Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 165 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved96
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1f1ab12-2663-4804-88a6-9cdf23734462 · outbound

This paper cites Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

Reference 1

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source=arxiv_source observed=2026-08-10T23:40:18.643266Z digest=sha256:1b8d0f3298c679662ee7dd58e51c7d09ddb13487bcebcc5b36e888ca7b17dd09

Observation d0e32831-56dd-4011-96fc-38921f5d6463 · outbound

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

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 2

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source=arxiv_source observed=2026-08-10T23:40:18.648145Z digest=sha256:c699daf9ed790649965c997bc4b8cb5171e940bfaba6b45e004054cfc8b584ec

Observation 43002725-a93c-40b7-bc7a-e7e49512140b · outbound

This paper cites , author Johnson, W.A.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Johnson, W.A

Reference 3

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source=arxiv_source observed=2026-08-10T23:40:18.652053Z digest=sha256:e7b9fa878128398adff9ae787655e099aebbb95ebf1112cd202064844d6e9bb2

Observation 5f8452dd-05aa-4dad-9952-9200e71b34ea · outbound

This paper cites , author Mahmoud, M.M.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Mahmoud, M.M

Reference 4

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source=arxiv_source observed=2026-08-10T23:40:18.655780Z digest=sha256:84443fcc6a36a0c4d087202de0a36fc9a525699074794a14300fb80b03c8bd94

Observation 06e47585-a777-49c9-bdda-83837781c14f · outbound

This paper cites Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs

Reference 5

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source=arxiv_source observed=2026-08-10T23:40:18.659212Z digest=sha256:bbbe4b06e2059c147c7c236b79eedd4043b00518f0329a789797e08e88cf9ad9

Observation f296c00a-4809-499b-995b-ad6a2724722a · outbound

This paper cites Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits

Reference 6

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source=arxiv_source observed=2026-08-10T23:40:18.663559Z digest=sha256:64ea97377f2f6e71caa672802ab50ce301e9b0ce5ade58460bec4fb4df8af197

Observation 537888dd-05cd-4539-a3fe-422b4264b677 · outbound

This paper cites Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data

Reference 7

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source=arxiv_source observed=2026-08-10T23:40:18.667825Z digest=sha256:61528e5425c3ee7e3c8ca9a885734343c492ea1f7316b5c124a9fd3dc152ff39

Observation 76ab6ae8-d09a-4444-8743-dbc0009be52b · outbound

This paper cites Poisoning Attacks against Support Vector Machines.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Poisoning Attacks against Support Vector Machines

Reference 8

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source=arxiv_source observed=2026-08-10T23:40:18.671656Z digest=sha256:92db6f40cefb31d70fd6ef68d1ed7fd984e8d3b04e212a13741314b6f904f8c6

Observation 9aa9265e-02be-4542-9738-e091311e0dbe · outbound

This paper cites , year 2023.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , year 2023

Reference 9

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source=arxiv_source observed=2026-08-10T23:40:18.675071Z digest=sha256:343a5c1a41e86fdb96c2144e7b9586eac1878b33dcf0ecf1a9a7ba591a8fc81c

Observation b5c3f8fe-d654-4caa-a3cb-82ea829ce422 · outbound

This paper cites , author Mann, B.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Mann, B

Reference 10

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source=arxiv_source observed=2026-08-10T23:40:18.679045Z digest=sha256:cb590c348400457c3628ab0185b3701dd3f94ad08786bb6e7e8e6adf8b2951c4

Observation eb5525ef-4f77-4859-9238-b125e3fab6c7 · outbound

This paper cites Membership Inference Attacks From First Principles.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Membership Inference Attacks From First Principles

Reference 11

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source=arxiv_source observed=2026-08-10T23:40:18.682934Z digest=sha256:e0c0c9269787c325b3b0dd09ea5d1ca3d2d0e4ea515a44cdc3a93be4d48561b1

Observation 6b25a6d7-7e87-4e67-968d-108b2ef9fe48 · outbound

This paper cites , author Tram \`e r, F.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Tram \`e r, F

Reference 12

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source=arxiv_source observed=2026-08-10T23:40:18.686543Z digest=sha256:e2f1b78979ffbb53b37208d362b5a2359f0b8c3d1e283931d5a2ff8ef290f5bc

Observation 92ed08c3-6728-46cd-bf91-4b5ff8c5d0b0 · outbound

This paper cites , author Wagner, D.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Wagner, D

Reference 13

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source=arxiv_source observed=2026-08-10T23:40:18.689874Z digest=sha256:38a05f8520148dd97d1a0903b3cc8bd43365c54824c632cdd6eed88ecb3976e2

Observation bb2d5a19-6c9d-436d-ae4b-22cb5bf437df · outbound

This paper cites , author Wagner, D.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Wagner, D

Reference 14

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source=arxiv_source observed=2026-08-10T23:40:18.693442Z digest=sha256:2809296c64b6890f17ab322688bb7ad8727a952aae72aa8ec3f08154e12e8431

Observation 2717099a-c1f4-4e70-9ed7-daf610bd9cfe · outbound

This paper cites , author Wagner, D.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Wagner, D

Reference 15

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source=arxiv_source observed=2026-08-10T23:40:18.696722Z digest=sha256:543e5cb37b0cbc0d10891d22357de46cd839686866c7cd8437baf5045543a967

Observation 9742ba3f-60e1-4e69-a314-a05c321b92eb · outbound

This paper cites , author Wagner, D.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Wagner, D

Reference 16

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source=arxiv_source observed=2026-08-10T23:40:18.699982Z digest=sha256:efe7a6b7f47cb93ec86224ef0d727778c5489998d0bbe6dcc0863ee6e1b3e996

Observation 7d004fd5-9ea8-4ea0-96b2-d3d7b384fc68 · outbound

This paper cites Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 17

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source=arxiv_source observed=2026-08-10T23:40:18.703164Z digest=sha256:f82e28b925c855231155ed90a76e0424de76c4e679d5cb6f0a0a3ebc3696136f

Observation 065356a3-5bd0-434b-9618-b923c771cbfe · outbound

This paper cites , author Danger, J.L.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Danger, J.L

Reference 18

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T23:40:18.706525Z digest=sha256:28fec1d13a0ad51e79c982b5c8d26897c217d074ddb15cd006d04a361783a132

Observation 35decfe3-3094-4e20-b53b-8ab9f78cedc3 · outbound

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

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 19

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source=arxiv_source observed=2026-08-10T23:40:18.709799Z digest=sha256:81d8f4c429d40def7be8f8d6978383b9a17ec2863e96af3bff92efc5e222aa7e

Observation e17df67c-109f-45de-890b-f4a1f1590844 · outbound

This paper cites , author Fu, C.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Fu, C

Reference 20

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source=arxiv_source observed=2026-08-10T23:40:18.713455Z digest=sha256:276947df62df958dfc2f15b759e585f24e570b38ec0a294004a53d158be7b846

Observation 6d0f585d-e345-41f6-a19d-5705f5427ae0 · outbound

This paper cites , author Zeng, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Zeng, Y

Reference 21

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source=arxiv_source observed=2026-08-10T23:40:18.716788Z digest=sha256:e140285c9d58970e15841316088a9c2cc08777353d59a995b7a0f822be3b155c

Observation cbbb07e2-36fa-451b-8135-3d3976b732b1 · outbound

This paper cites , author Barron, T.M.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Barron, T.M

Reference 22

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source=arxiv_source observed=2026-08-10T23:40:18.719962Z digest=sha256:ca3310fbd77ffc421e5bfdc49ae35f718772bdf7784802023e1e0bfa7b3906b7

Observation 998e4cfe-f358-405d-9a69-2be9d8a00a27 · outbound

This paper cites ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models

Reference 23

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source=arxiv_source observed=2026-08-10T23:40:18.723258Z digest=sha256:fc5e07381afdb606f28a86c547208126ae1c0d21ba382345e71b3acc72d0d666

Observation 4488fc2b-7faa-4164-8ae8-c30613d10a05 · outbound

This paper cites JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs

Reference 24

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source=arxiv_source observed=2026-08-10T23:40:18.726600Z digest=sha256:ce2cd339000984f1fe63482d65a7dc39a968c1d0ff6f839f2a4cfbd9f5f83102

Observation b4a4ee5b-1d72-45d1-9efe-4052addf347a · outbound

This paper cites , author Li, H.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Li, H

Reference 25

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source=arxiv_source observed=2026-08-10T23:40:18.729937Z digest=sha256:15c4ab055670b1ace11a0ee099c92818900f72c5b3e4b307b73f070c943e6958

Observation 9e6fd331-f679-435b-9e95-96609f323033 · outbound

This paper cites , author Tariq, A.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Tariq, A

Reference 26

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source=arxiv_source observed=2026-08-10T23:40:18.733279Z digest=sha256:d0e720fca3d73197662b00fc125dd08f1b8154b388e859ff8a424706c38e2b34

Observation cf6edc23-015f-4d28-be21-1668db79df10 · outbound

This paper cites MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots

Reference 27

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source=arxiv_source observed=2026-08-10T23:40:18.736547Z digest=sha256:0af032c339df309c32e5facaa2ec7c7da39bb11930f886521e1495338efb4a69

Observation af825157-d09d-4b32-9768-e65ac4347c0f · outbound

This paper cites , author Liu, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Liu, Y

Reference 28

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source=arxiv_source observed=2026-08-10T23:40:18.739598Z digest=sha256:11aefe219fd570ca595a07e6c86ea830572b085fad63f5636e35e30a7e58d968

Observation 87326a0b-1d70-481e-a778-d7ebb6f50084 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 29

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source=arxiv_source observed=2026-08-10T23:40:18.742729Z digest=sha256:87f53f25471c182b20e7af02eceddb5061657a80924cf3fe2e0bc9e9dc219760

Observation 4626db44-3609-44af-9a0d-29f3df6d4ecc · outbound

This paper cites A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily

Reference 30

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source=arxiv_source observed=2026-08-10T23:40:18.746365Z digest=sha256:73788e711fef3c74be3555215261f0e02d44f41f2cd53eb8d8a5f354440c0d11

Observation 68778429-d226-4133-b06c-c651075a60ff · outbound

This paper cites , author Tian, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Tian, Y

Reference 31

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source=arxiv_source observed=2026-08-10T23:40:18.749962Z digest=sha256:d23328a55ed209662ba583a432b027553fb81495f50b4270668eebd52f2f6d4d

Observation 0388e491-9e51-4afc-a5d8-8809c860e762 · outbound

This paper cites , author Dong, J.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Dong, J

Reference 32

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source=arxiv_source observed=2026-08-10T23:40:18.753216Z digest=sha256:6966da6bfcd2bb23bb15158cd5da3bec274f5a5806406a5b3489a17e013fcd4b

Observation d80469e7-7dd0-498e-9915-24ddaf3628e5 · outbound

This paper cites , author Liao, F.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Liao, F

Reference 33

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source=arxiv_source observed=2026-08-10T23:40:18.756273Z digest=sha256:1f0ae8224a47cfa28ba3d5e3b3a8ec16c5b6e28e9d8898fd9c68436122ecb2d9

Observation c5b35189-4123-4972-9ed0-3ddcbb2b988e · outbound

This paper cites , author Zhao, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Zhao, Y

Reference 34

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source=arxiv_source observed=2026-08-10T23:40:18.759522Z digest=sha256:dd0e36f1d8bdeaa4c95eb090e7dd793a7f8ab367f572531aad30bfe054f8c236

Observation 4e2b03e1-1221-41b8-97ea-59b72c911c25 · outbound

This paper cites Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak

Reference 35

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source=arxiv_source observed=2026-08-10T23:40:18.762886Z digest=sha256:7b2f6ec7381bf1dbf8bdd4362bc2fa2f6a61e5ea20a2b32c90ed7ba6460f441a

Observation 258a3b3b-3f90-4d57-b407-59c910c9a5f5 · outbound

This paper cites DE-COP: Detecting Copyrighted Content in Language Models Training Data.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 36

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source=arxiv_source observed=2026-08-10T23:40:18.766132Z digest=sha256:f744555a39451de88355f3c0205453531b9c43fa07f1be6029549b3ee4468402

Observation 31908427-ed57-476f-9e55-c31dd8c7de64 · outbound

This paper cites The Llama 3 Herd of Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs The Llama 3 Herd of Models

Reference 37

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source=arxiv_source observed=2026-08-10T23:40:18.769741Z digest=sha256:de2b34b69848c26715d47c573145c971f88f1e6ce0f17be5aa9b242c767e4a9c

Observation 14a19a5b-122e-4696-9a8b-04eb27e12a93 · outbound

This paper cites HotFlip: White-Box Adversarial Examples for Text Classification.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs HotFlip: White-Box Adversarial Examples for Text Classification

Reference 38

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source=arxiv_source observed=2026-08-10T23:40:18.773523Z digest=sha256:2e0444f454b20df70e32f971bc337d539d4c473269b461fe0eb27922b946a516

Observation 219132aa-dc5f-4f2e-826b-ed91ea881ba6 · outbound

This paper cites Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems

Reference 39

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source=arxiv_source observed=2026-08-10T23:40:18.776940Z digest=sha256:f2ec6976a02eaa3dff9df1e859e3ee757392854daca7b9aab30cbf70cf291a86

Observation fc04d8c1-0166-4993-9487-0f6b4f4dc192 · outbound

This paper cites , author Evtimov, I.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Evtimov, I

Reference 40

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source=arxiv_source observed=2026-08-10T23:40:18.780338Z digest=sha256:cae62c6a4aa11df94ddbe78f670c976c3c562cf5ea93cedc7e896c5fcfeeef60

Observation fdbd7952-eb6b-4dac-bda4-b1ec1364f8f4 · outbound

This paper cites , author Wang, B.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Wang, B

Reference 41

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source=arxiv_source observed=2026-08-10T23:40:18.784181Z digest=sha256:4f30507060555e6fec29c132557e9d66b795826738703bc4dfe59c074044ff20

Observation 2f1541cd-5554-4ec4-ae2f-d6e797f6d8ce · outbound

This paper cites , author Bowers, J.D.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Bowers, J.D

Reference 42

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source=arxiv_source observed=2026-08-10T23:40:18.787427Z digest=sha256:3669d30612a58348f3048c1efaeeaf5721a52d42ce6207b92306bd4a893bf413

Observation d70d7df3-86f5-42fd-b142-6c0765682c7f · outbound

This paper cites title C V S S v3.1 U ser G uide --- first.org.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs title C V S S v3.1 U ser G uide --- first.org

Reference 43

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source=arxiv_source observed=2026-08-10T23:40:18.790573Z digest=sha256:4f8c6e832d428b904876398a3ee9ee5ceed187c754c404be790ec37a60a99e40

Observation 1657f171-333a-4df9-8c06-769c5f9b8c6a · outbound

This paper cites Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 44

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source=arxiv_source observed=2026-08-10T23:40:18.793634Z digest=sha256:76bb131791f4a7e72c0ef263253603533400c6c18b172a87fd4a56597969cb0b

Observation 51aa7fb1-1267-407a-b78e-6f24ba9f8bc5 · outbound

This paper cites , author Ramakrishnan, G.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Ramakrishnan, G

Reference 45

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source=arxiv_source observed=2026-08-10T23:40:18.796958Z digest=sha256:f79700daac9cab9619f19016d947c10469c81204c2d5b328ba9d362b342929a3

Observation 51302d53-8ecb-49fe-9bd4-df7e570cd22c · outbound

This paper cites Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Reference 46

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source=arxiv_source observed=2026-08-10T23:40:18.800470Z digest=sha256:87c45810affd6f34be50da6fad7cc0377a51ba5f8e8e27dfef55755c14c0054f

Observation d32d0ce5-0404-4151-80cb-80fb5e2ceb31 · outbound

This paper cites Attacking Large Language Models with Projected Gradient Descent.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Attacking Large Language Models with Projected Gradient Descent

Reference 47

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source=arxiv_source observed=2026-08-10T23:40:18.803840Z digest=sha256:389ad1084c1ccb5509037b48dfb7ed9bee4973d44530d3ddd3b1c2665bada821

Observation a5b0f19c-07a5-48b1-aaca-192dd707d129 · outbound

This paper cites , author \"O zuysal, M.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author \"O zuysal, M

Reference 48

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source=arxiv_source observed=2026-08-10T23:40:18.807134Z digest=sha256:9b757276c90ef60e8a2853ce68a92b9db3ed69e20924dafe1c8f12bb39804ae4

Observation d742e975-c81c-47e9-aa98-a6c015c97fcf · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Explaining and Harnessing Adversarial Examples

Reference 49

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source=arxiv_source observed=2026-08-10T23:40:18.810215Z digest=sha256:3be524446f5ceba75181e728a12497282df9af0c3dd1e8763bcfc768d92f368e

Observation bf5736f7-5443-4adf-b73e-266f23064d0b · outbound

This paper cites Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Reference 51

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source=arxiv_source observed=2026-08-10T23:40:18.817469Z digest=sha256:d7c19124d825581e1b501e39aeb062784f124db4177b928ded3aacbe2f38209b

Observation 114fc76c-dfeb-4aed-88df-bb5839a20ccd · outbound

This paper cites , author Liu, K.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Liu, K

Reference 52

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source=arxiv_source observed=2026-08-10T23:40:18.820493Z digest=sha256:2eae6bd1cd4707ecd450697d9840d69fa18613126d79e540cfbef3b10285135a

Observation 2dbe7eae-7739-4e0e-9532-272258a68256 · outbound

This paper cites Gradient-based Adversarial Attacks against Text Transformers.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Gradient-based Adversarial Attacks against Text Transformers

Reference 54

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source=arxiv_source observed=2026-08-10T23:40:18.827110Z digest=sha256:eab28d0f1bea87c4c1a7c7905efdc86a2948a03655874d86463e16cd5626083f

Observation 3a0a79c3-723a-44f8-953a-e9ddaf6d0285 · outbound

This paper cites Query-Based Adversarial Prompt Generation.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Query-Based Adversarial Prompt Generation

Reference 55

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source=arxiv_source observed=2026-08-10T23:40:18.830032Z digest=sha256:93b799993b7da6ae8de9dc4423cbd2056f6dbcaa4292b811bc4f412e95e91c61

Observation 76ddbcf3-fc31-462e-a04d-7fe748d2bf35 · outbound

This paper cites , author Salcic, Z.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Salcic, Z

Reference 56

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no resolver link, observed 2026-08-10T23:40:18.833422Z

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source=arxiv_source observed=2026-08-10T23:40:18.833422Z digest=sha256:d13bcc0188070a07b90703bfbe45026d34649d94ef00d378696f44c40adc031a

Observation d3f05bab-503d-4a29-9f60-4da162086408 · outbound

This paper cites Composite Backdoor Attacks Against Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Composite Backdoor Attacks Against Large Language Models

Reference 57

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source=arxiv_source observed=2026-08-10T23:40:18.836509Z digest=sha256:72a5fee0ea921ab51d0e2fcd3f16ae604807482ea9be5c21080a1a56b635a25d

Observation 54392d6c-533a-4cc2-8755-ade2635be6fc · outbound

This paper cites , author Geiping, J.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Geiping, J

Reference 58

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source=arxiv_source observed=2026-08-10T23:40:18.839850Z digest=sha256:8365b1c7a66c9390933e2ebc3474f7d8615f9dbb579310fa5d20274a72c2350c

Observation 7167bb90-eae9-432e-a68d-fd067608cda2 · outbound

This paper cites Black-Box Adversarial Attack with Transferable Model-based Embedding.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Black-Box Adversarial Attack with Transferable Model-based Embedding

Reference 59

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source=arxiv_source observed=2026-08-10T23:40:18.843186Z digest=sha256:051651ae96c98c8ec35a60cb581e56bd495250b1bf3579d0b3f4df33747e2f17

Observation 4482f886-8824-498e-810d-bf44651e00dc · outbound

This paper cites The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

Reference 60

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source=arxiv_source observed=2026-08-10T23:40:18.846600Z digest=sha256:068bf7701d2eee82347661869534ed9cbaf5f0f1dc36428cf680a9b8589bbd3e

Observation fd6b2a76-091d-40ba-93ea-380fdbfb685b · outbound

This paper cites , year 2022.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , year 2022

Reference 61

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source=arxiv_source observed=2026-08-10T23:40:18.850085Z digest=sha256:0ed5fd12e94157808c9cfe1da2bca9435c1525a3873c0100e6237dabaf97c36e

Observation afd32ca4-0643-4926-b39d-00ac5b948e85 · outbound

This paper cites , author Carlini, N.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Carlini, N

Reference 62

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source=arxiv_source observed=2026-08-10T23:40:18.853296Z digest=sha256:98ea1c0ec0bc607a92f636e23537a12ba4792e7b06e4391c04a99f4e4b0bd3d0

Observation fc0b7669-97f2-4286-b7df-addabbcfd847 · outbound

This paper cites Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs

Reference 63

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source=arxiv_source observed=2026-08-10T23:40:18.856384Z digest=sha256:0556ad2938442439bd8fb083af830192e25fef363a6fc002d576ca5e66ce4282

Observation b24ae9a6-a797-4dc5-972e-4da17757e871 · outbound

This paper cites ProPILE: Probing Privacy Leakage in Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs ProPILE: Probing Privacy Leakage in Large Language Models

Reference 64

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source=arxiv_source observed=2026-08-10T23:40:18.859847Z digest=sha256:c75b4459d933fbeebbe698b322eacafb325aea0919aa54e34716a782161e64da

Observation b1d2ffc2-ae93-4667-bcc7-2d7e1f5abd91 · outbound

This paper cites Certifying LLM Safety against Adversarial Prompting.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Certifying LLM Safety against Adversarial Prompting

Reference 65

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source=arxiv_source observed=2026-08-10T23:40:18.863343Z digest=sha256:f25aa17ef82d7a4472e96dc021d3a818a7f42f7bbad5f1f9ea91a79cafbbd5df

Observation 8031f3da-587d-4b80-9b4d-3868a96c84e2 · outbound

This paper cites , author Mohan, C.K.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Mohan, C.K

Reference 66

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no resolver link, observed 2026-08-10T23:40:18.866851Z

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source=arxiv_source observed=2026-08-10T23:40:18.866851Z digest=sha256:906d37c205d45a38bb2c231a8f017b234919aa800a0507cd2b8b6cef4cb51bd3

Observation e316d26c-b291-4bcb-b894-5730f078520c · outbound

This paper cites , year 2024.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , year 2024

Reference 67

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source=arxiv_source observed=2026-08-10T23:40:18.870152Z digest=sha256:5ea6d4589b19ed3cb8d0345b78ba12bb9be58de91e1d718ebdfebfda6fea0728

Observation 0b35db98-10f5-4da9-b167-7d6129c37a14 · outbound

This paper cites Adversarial examples in the physical world.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Adversarial examples in the physical world

Reference 68

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source=arxiv_source observed=2026-08-10T23:40:18.873300Z digest=sha256:e682e388f84d0543d2c198618f891692a9614a9a5ee6e4d7117ad081844a8340

Observation 6172f59d-8acd-457e-819a-781f6d2a8980 · outbound

This paper cites Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems

Reference 69

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no resolver link, observed 2026-08-10T23:40:18.876912Z

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source=arxiv_source observed=2026-08-10T23:40:18.876912Z digest=sha256:5a936c43aee552ab30276badfd8ed83268388797254f5509c42f1eec3a51354d

Observation 897a4fa3-f174-450e-81e2-c7ee2f4b9600 · outbound

This paper cites Multi-step Jailbreaking Privacy Attacks on ChatGPT.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Multi-step Jailbreaking Privacy Attacks on ChatGPT

Reference 70

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no resolver link, observed 2026-08-10T23:40:18.880696Z

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source=arxiv_source observed=2026-08-10T23:40:18.880696Z digest=sha256:6e567073cd7a20b824f5c17cb55faa3290efc22d0d1978f304b0340081f15ccb

Observation edf6d45c-388a-4588-9f95-f13e89e0706e · outbound

This paper cites , author Ma, R.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Ma, R

Reference 71

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no resolver link, observed 2026-08-10T23:40:18.884172Z

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source=arxiv_source observed=2026-08-10T23:40:18.884172Z digest=sha256:7c8bb325a88ccbc5d5e79e67d748ae60af798abcb14c97097fc3bb18493818e9

Observation 19e3eda7-2088-46a3-a9b0-766a2bebc135 · outbound

This paper cites , author Xue, M.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Xue, M

Reference 72

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no resolver link, observed 2026-08-10T23:40:18.887395Z

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source=arxiv_source observed=2026-08-10T23:40:18.887395Z digest=sha256:f4ccf13eee70a63ec9d0e8c9cc0284b7f81b01eb95c0424f926518232ff5365a

Observation 27dfe993-1704-4c34-b01a-3ff8c08294e1 · outbound

This paper cites DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers

Reference 73

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source=arxiv_source observed=2026-08-10T23:40:18.890566Z digest=sha256:9306461f136c83c6afe87fd3e7549baf28751d706d267ec9535c11275208f33e

Observation 718937eb-9807-4cec-aaf6-850a2a83479a · outbound

This paper cites BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models

Reference 74

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no resolver link, observed 2026-08-10T23:40:18.894906Z

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source=arxiv_source observed=2026-08-10T23:40:18.894906Z digest=sha256:e6ec0ab25604221256118e80ab5468bc7c3b3111d9bc6f1cbd86caed043a7817

Observation 67d21510-4af1-4e4d-b148-2da5ae8f0890 · outbound

This paper cites Backdoor Attack in the Physical World.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Backdoor Attack in the Physical World

Reference 75

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no resolver link, observed 2026-08-10T23:40:18.898803Z

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source=arxiv_source observed=2026-08-10T23:40:18.898803Z digest=sha256:9849645a32ec628ebc19cd8b92f0e2e39ad8788f50a97b0bab2a547d9a80942d

Observation ed7036de-ae3a-4ce6-8128-a2c0a3a140f5 · outbound

This paper cites TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models

Reference 76

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verified exact
local_arxiv, observed 2026-08-10T23:40:20.403205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T23:40:18.902560Z digest=sha256:4fbdc95598a4602b9ab4b45b2150ed7770a5112427f868ad18b301f39d67d3ef

Observation 3d040eb5-57ac-420f-8de7-d075e855c493 · outbound

This paper cites , author Shi, C.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Shi, C

Reference 77

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source=arxiv_source observed=2026-08-10T23:40:18.906248Z digest=sha256:92db436de8def93c296349b69b02edc993a8d3fb52a43a08ab50933b8348deac

Observation 2b30b9ad-49f6-420e-9d19-c4dd844e61ab · outbound

This paper cites Exploring Vulnerabilities and Protections in Large Language Models: A Survey.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Exploring Vulnerabilities and Protections in Large Language Models: A Survey

Reference 78

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source=arxiv_source observed=2026-08-10T23:40:18.909862Z digest=sha256:9003f50638fb633835e628a7a48bd297dc90770b29c3fcca78b0d459c6298b1f

Observation 683b798a-ac00-4d1d-ae37-753547f53b83 · outbound

This paper cites , author Jia, J.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Jia, J

Reference 79

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source=arxiv_source observed=2026-08-10T23:40:18.913451Z digest=sha256:4c838389b4b76d56775c02d372e916ebd066f97760f45cb04ad201f025e07c02

Observation b7121946-b6ef-4373-9fe0-e8f2fdb40a3d · outbound

This paper cites Boosting Gradient for White-Box Adversarial Attacks.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Boosting Gradient for White-Box Adversarial Attacks

Reference 80

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local_arxiv, observed 2026-08-10T23:40:20.296171Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T23:40:18.917039Z digest=sha256:bf5d9f81da42b176b073841f7db0f675c5db4c2d865f013ab2b50a7c33a824af

Observation c2a5c351-c166-4b74-a897-bcce57595327 · outbound

This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 81

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source=arxiv_source observed=2026-08-10T23:40:18.920701Z digest=sha256:5119d2ab2e2d1e3d090e3c91d6ca1239ec60d45a5664bf947e56c62ef95e9534

Observation 2da4cd04-7c88-41c6-a3b5-c2a5c5873a7c · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 82

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source=arxiv_source observed=2026-08-10T23:40:18.924328Z digest=sha256:6a3ed1333b5987a1eafbbd493f206d353678f683ea042b2a625818005c66b7bb

Observation 2bc4f214-b188-4331-99c3-c590eafac6e3 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Prompt Injection attack against LLM-integrated Applications

Reference 83

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source=arxiv_source observed=2026-08-10T23:40:18.928326Z digest=sha256:98317e159a7ada2da6261c7d036f7fd8eeb3a3cd5515264aa6bf5ba771912db3

Observation ce7733dc-ceab-4b91-9d48-3fe34c291ae8 · outbound

This paper cites , author Jia, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Jia, Y

Reference 84

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source=arxiv_source observed=2026-08-10T23:40:18.931958Z digest=sha256:bf54fa91b404ed3591fc54815640986d858bb4fb7ebdc795581f780197826534

Observation 19cff4e7-b86b-43e6-bf14-224fc7f1a715 · outbound

This paper cites , author Jia, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Jia, Y

Reference 85

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source=arxiv_source observed=2026-08-10T23:40:18.935196Z digest=sha256:0b6c38944dcb656277ab8ae5f50b6a699d421526e1b9447264a663b678b71f5f

Observation 29c75329-0fdd-41b9-ada1-fb05190c464e · outbound

This paper cites , author Jia, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Jia, Y

Reference 86

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source=arxiv_source observed=2026-08-10T23:40:18.938581Z digest=sha256:b3181567f15ff04251297878183d67e167de35c35e103ff4d3e2d377c61cc7b4

Observation d8ac168e-e14c-4f8c-95f1-c525466d46a1 · outbound

This paper cites , author Mondal, A.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Mondal, A

Reference 87

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source=arxiv_source observed=2026-08-10T23:40:18.941944Z digest=sha256:46a058a92f225956538c96ce7928a53048f29463a49275739c98af4c913dff80

Observation 942d9250-36d1-4d78-b5a6-8b72d81a3dea · outbound

This paper cites Analyzing Leakage of Personally Identifiable Information in Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Analyzing Leakage of Personally Identifiable Information in Language Models

Reference 88

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source=arxiv_source observed=2026-08-10T23:40:18.944927Z digest=sha256:a2772a970a443d82f664619219d1648a64a9ad38425b16cf665ec2dde7c01af3

Observation 67145140-da1c-4493-a477-07bb6839830b · outbound

This paper cites , author Li, W.J.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Li, W.J

Reference 89

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source=arxiv_source observed=2026-08-10T23:40:18.949043Z digest=sha256:50ab447202ead83f3b40a290f3e4ecf8245173c6bf18a90a4588ba7e542d8cf5

Observation 71a346b9-e3b9-43b7-83e5-e27a7376d0ea · outbound

This paper cites Membership Inference Attacks against Language Models via Neighbourhood Comparison.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Membership Inference Attacks against Language Models via Neighbourhood Comparison

Reference 90

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source=arxiv_source observed=2026-08-10T23:40:18.952415Z digest=sha256:6a1b4b5d334ba741445dee73284e486cf21ab4b11b59eb489a9c127a05f102b6

Observation 233bee87-905b-4e7b-88e7-2402e70b43ee · outbound

This paper cites , author Steve, L.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Steve, L

Reference 91

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source=arxiv_source observed=2026-08-10T23:40:18.955689Z digest=sha256:4da18659949c89cd551cb41e9fd8b3d2193c12808de90b9d8bf9946a71a97fe0

Observation eb16f093-f602-4130-a882-2799ea48da76 · outbound

This paper cites , year 2023.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , year 2023

Reference 92

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source=arxiv_source observed=2026-08-10T23:40:18.958733Z digest=sha256:e823758ff3839599a6a83a89a3184c61c5dcee64b3a10c241996dd219023f254

Observation 92f7400e-7854-4cce-b05a-087f593435f2 · outbound

This paper cites , author Wang, B.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Wang, B

Reference 93

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source=arxiv_source observed=2026-08-10T23:40:18.961710Z digest=sha256:257e170d6680366074c1301686581b0d98c2c33d2c5b9dc1158ad40a40e84685

Observation 4373f005-c79d-4288-a495-eb3de3de662a · outbound

This paper cites PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding

Reference 94

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source=arxiv_source observed=2026-08-10T23:40:18.965335Z digest=sha256:4d87c22c9806a24c24e3b4df6aff6ea6f2f2f902f8833d2ff22997f94224e899

Observation a5158f09-dcc4-4a7b-9e70-c02f04be938c · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Scalable Extraction of Training Data from (Production) Language Models

Reference 95

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source=arxiv_source observed=2026-08-10T23:40:18.968749Z digest=sha256:e4d7c973251e9e09a2f1075b2931448d1c282b0e1ce3082ba9edead69a27b48f

Observation f2021cd5-baf3-4395-99e2-64ae2e8cd5df · outbound

This paper cites , author Sun, Y.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Sun, Y

Reference 96

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verified exact
raw_fallback, observed 2026-08-10T23:40:20.050402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T23:40:18.972053Z digest=sha256:fc73c3f6a0d365980d74a7c0f045452a0cec6391a928fbee2108d15a37af2c88

Observation 6e75aa94-4981-46bd-bb86-1df184d3ea97 · outbound

This paper cites title H ello G P T -4o.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs title H ello G P T -4o

Reference 97

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source=arxiv_source observed=2026-08-10T23:40:18.975267Z digest=sha256:498349f6eb0f4a39197499f387d9689827c47b043fd727073fb3e8efe48e2e96

Observation 7b66e0a8-a6c1-4cbc-a50f-7564b9d075d4 · outbound

This paper cites , author Vassilev, A.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author Vassilev, A

Reference 98

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source=arxiv_source observed=2026-08-10T23:40:18.978234Z digest=sha256:a777bec7bb7ecc173294b9f1adc3028cbaacea495fd20312d2f9387d43b83891

Observation 05f19177-8b6d-4fbc-a061-00f08efe4a18 · outbound

This paper cites title O W A S P T op 10 for L arge L anguage M odel A pplications | O W A S P F oundation --- owasp.org.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs title O W A S P T op 10 for L arge L anguage M odel A pplications | O W A S P F oundation --- owasp.org

Reference 99

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source=arxiv_source observed=2026-08-10T23:40:18.981225Z digest=sha256:3806f470154df3718535df4f975b005074d88f2cfb82fd737db7c578c9467a53

Observation 06b98651-c07e-4288-95b3-ed179da631b4 · outbound

This paper cites , author McDaniel, P.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs , author McDaniel, P

Reference 100

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source=arxiv_source observed=2026-08-10T23:40:18.984401Z digest=sha256:bf2c9686a707398113c6e9e0768260cba188b0dd8e9ee4250595d68693614b2e

Observation 3ff3eb2f-0da3-447d-848b-9e1c7bcc26fe · outbound

This paper cites Jailbreaking and Mitigation of Vulnerabilities in Large Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

Reference 101

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no resolver link, observed 2026-08-10T23:40:18.987383Z

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source=arxiv_source observed=2026-08-10T23:40:18.987383Z digest=sha256:ac01134e8871739a7ceaa94975d3082f7ce5aaa316507415245b6482a4072ada

Observation 4f6d86b8-3a4a-4cc4-8197-8f5a7532d86e · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Ignore Previous Prompt: Attack Techniques For Language Models

Reference 102

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source=arxiv_source observed=2026-08-10T23:40:18.992038Z digest=sha256:ae3554fbbc027eef5441f4df6faf29e0297c863208b3d5f2e0a20783967d69f3

Pith citing papers

Observation 2182fa6b-c542-427b-b76a-fb0c1158b3d8 · inbound

How Well Do AI Systems Solve AP Physics? A Comparative Evaluation of Large Language Models on Algebra-Based Free Response Questions cites this paper.

How Well Do AI Systems Solve AP Physics? A Comparative Evaluation of Large Language Models on Algebra-Based Free Response Questions On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs

Reference 28

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no resolver link, observed 2026-07-15T13:15:31.225992Z

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source=pdf_text observed=2026-07-15T13:15:31.225992Z digest=sha256:c6b1e361bcceeee74bebf642b248beb83c19114f6e0b46e9fb69656802746210