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Paper Citation Record · LEDGER

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries

As of 22 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.18932.

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

pith.paper-citation-record.v1
2506.18932 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:04:41.423735Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T15:11:34.346680Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:37:35.433048Z

Reference resolution

51 of 51 outbound references displayed

  • verified exact0
  • verified fuzzy30
  • unresolved20
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5e4ca85-fa42-4a37-a01e-623179615aaf · outbound

This paper cites The role of machine and deep learning in modern intrusion detection systems: A comprehensive review.Computers and Electrical Engineering, 124:110318, 2025.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries The role of machine and deep learning in modern intrusion detection systems: A comprehensive review.Computers and Electrical Engineering, 124:110318, 2025

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:41.089660Z digest=sha256:d9195d8993a504a162112406a54a608bc59b68501ad94d9c74b07964b2f15a45

Observation d686d80f-3854-4f24-a79a-09b5af85b817 · outbound

This paper cites Concrete Problems in AI Safety.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Concrete Problems in AI Safety

Reference 2

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source=pdf_text observed=2026-08-15T19:04:41.101116Z digest=sha256:96a13a1b1400ee571aa0dcf1800a2af54abdef12c8840d07327e47b5fc0adbb2

Observation 897cda50-382a-4454-9515-857e843b1cb0 · outbound

This paper cites Anderson.Security Engineering: A Guide to Building Dependable Distributed Systems.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Anderson.Security Engineering: A Guide to Building Dependable Distributed Systems

Reference 3

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.110897Z digest=sha256:77c15dc41804261ed6516a0fbe4ca13667a6d775693f2a7d9418b5b831693ea4

Observation f1e62e00-450e-4755-a32a-985beb5db4c4 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Constitutional AI: Harmlessness from AI Feedback

Reference 4

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source=pdf_text observed=2026-08-15T19:04:41.117356Z digest=sha256:f3cf3ec4556f5e0908ade1ad5dfa3877a8f5e871377d6e218f75b2cd9f6dab1c

Observation 7f49381b-2b33-435f-bbec-1567e7273c4c · outbound

This paper cites fairmlbook.org, 2019.http://www.fairmlbook.org.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries fairmlbook.org, 2019.http://www.fairmlbook.org

Reference 5

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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.

source=pdf_text observed=2026-08-15T19:04:41.122027Z digest=sha256:a45d5f5456b75311b288bb725e422b693400d69bc0771f5aeaa480240b975d1d

Observation 57128cc4-8cba-409f-bc2e-4c29ca1817ca · outbound

This paper cites International AI safety report: The international scientific report on the safety of advanced AI.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries International AI safety report: The international scientific report on the safety of advanced AI

Reference 6

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raw_fallback, observed 2026-08-15T19:04:42.481933Z

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.

source=pdf_text observed=2026-08-15T19:04:41.127790Z digest=sha256:e7314954b0947deab88384f32ed935382fc062e4b13158021c693abcbebd66e5

Observation 1938cab2-5c10-4a37-8bb3-bf4798e51f07 · outbound

This paper cites Poisoning attacks against support vector machines.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Poisoning attacks against support vector machines

Reference 7

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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.

source=pdf_text observed=2026-08-15T19:04:41.133406Z digest=sha256:0e6486e4e56c92472a9c56d0accd34cc9b0e29219ba0d9a0ba08d26aebb4b6c9

Observation d6702c3b-3e79-481d-bd25-41f990dec05d · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries On the Opportunities and Risks of Foundation Models

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T19:04:41.138725Z digest=sha256:0fcbc163f17ae67adc6d3f4b41359b4f6a78f21ee68eae65705958fe9f4aaed5

Observation 7614798f-953c-4258-bd7d-23bdd2e08fec · outbound

This paper cites The social dilemma of autonomous vehicles.Science, 352(6293):1573–1576, 2016.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries The social dilemma of autonomous vehicles.Science, 352(6293):1573–1576, 2016

Reference 9

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source=pdf_text observed=2026-08-15T19:04:41.143074Z digest=sha256:d449249688f8bc84f9480055cc1112e347d390c47feaf1061fe83ad6459edb01

Observation 125383af-2076-4b1a-a5cd-7b8314f64525 · outbound

This paper cites Oxford University Press, 2016.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Oxford University Press, 2016

Reference 10

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.148524Z digest=sha256:feed5b5992d7a819c9f5e307379f716095bb8071d2546abb3d2c90515f90aaa4

Observation 8c6462ef-1610-4b9f-b58a-e0b50eb7587d · outbound

This paper cites Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Reference 11

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source=pdf_text observed=2026-08-15T19:04:41.154859Z digest=sha256:c2bd9de965d34f632c19ea2bb4d91d73c4e77e8d83f2eabcf9500e27694b482d

Observation 3872207a-00ee-4e8e-a525-673fa445dc6b · outbound

This paper cites Towards evaluating the robustness of neural networks.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Towards evaluating the robustness of neural networks

Reference 12

Resolution
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raw_fallback, observed 2026-08-15T19:04:42.407685Z

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.

source=pdf_text observed=2026-08-15T19:04:41.161809Z digest=sha256:f2e13f51f05ca11ef165f63575692b887b88d549ee751f28b8c56935f405ecaf

Observation 7c4685da-bd8d-4541-9bf9-ab8aa6a4d96c · outbound

This paper cites Extracting training data from large language models.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Extracting training data from large language models

Reference 13

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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.

source=pdf_text observed=2026-08-15T19:04:41.167769Z digest=sha256:6b5c7fd5cc8493eab5c4135546cb41c515bc70654d4b8bd0ef8245807ccef72c

Observation c74d9e15-2d31-4237-a23f-5279ff539386 · outbound

This paper cites Is Power-Seeking AI an Existential Risk?.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Is Power-Seeking AI an Existential Risk?

Reference 14

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source=pdf_text observed=2026-08-15T19:04:41.177027Z digest=sha256:8fa67668a53b736f1212ec88c6a99f8a97a4a4c8135c93085b8467da29cf9498

Observation 87d612bc-1c63-469e-8684-31e3b6e2c7aa · outbound

This paper cites Privacy by design: The 7 foundational principles.Information and privacy commissioner of Ontario, Canada, 2009.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Privacy by design: The 7 foundational principles.Information and privacy commissioner of Ontario, Canada, 2009

Reference 15

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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.

source=pdf_text observed=2026-08-15T19:04:41.183410Z digest=sha256:d82b621cd538b0aebe3db2690ae8a0bd9d41fb64b74933edea2aeadb585d4c51

Observation bedb9f1e-43e0-4494-bbf0-c9e5bde33082 · outbound

This paper cites Finlayson, John D.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Finlayson, John D

Reference 16

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source=pdf_text observed=2026-08-15T19:04:41.192382Z digest=sha256:8c2fbdac62475de17fb73e06db42d01c201e4284038794dbc671f19d0f0b506d

Observation ae99c5bd-02ce-49cf-851f-b115cc2f3f53 · outbound

This paper cites Ai4people—an ethical framework for a good ai society: opportunities, risks, principles, and recommendations.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Ai4people—an ethical framework for a good ai society: opportunities, risks, principles, and recommendations

Reference 17

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.203222Z digest=sha256:34e13a1c623d851413e55c17dcd023a38819c8c0159db4a5a60f418b83a6b9b8

Observation b34caaa8-f7b1-43ac-a39c-6ec2dc22574c · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Model inversion attacks that exploit confidence information and basic countermeasures

Reference 18

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source=pdf_text observed=2026-08-15T19:04:41.211226Z digest=sha256:ada28bf51f07347e2358acacf78e56e9e1f6cac7c46da8c2b47ed37733f16336

Observation ab4b8570-6328-48e8-8e7e-24d85c605d0b · outbound

This paper cites Artificial intelligence, values, and alignment.Minds and Machines, 30:411–437, 2020.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Artificial intelligence, values, and alignment.Minds and Machines, 30:411–437, 2020

Reference 19

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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.

source=pdf_text observed=2026-08-15T19:04:41.221493Z digest=sha256:040449254246c9a956ae3f4518e926b686099ea4700d665badfc639f8b2f1529

Observation fa35dbcb-e59c-4de4-a97e-1117fb9382ba · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 20

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source=pdf_text observed=2026-08-15T19:04:41.226958Z digest=sha256:89e73e93a02edff9e81ab9bfeaa9ae5b6d2a2bfe2b9b02864b50d65c638b0f50

Observation c73f2f42-0451-4c3f-8523-32a26c432397 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Explaining and Harnessing Adversarial Examples

Reference 21

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source=pdf_text observed=2026-08-15T19:04:41.231711Z digest=sha256:55d0baf891bbd8070d45e28057f5c35e61d3cccc2c4aeae54a20e28c507abbfa

Observation 775d370b-c8da-4af4-94a9-a08f66eb329a · outbound

This paper cites Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection

Reference 22

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.238130Z digest=sha256:fb4e69a75214bee5c903960a313fdb6a87b7310fbd1fffaa13a1ee02d9e97e02

Observation f8d4f59d-68a5-4361-bc3a-b51ec3a12178 · outbound

This paper cites XAI—explainable artificial intelligence.Science Robotics, 4(37), 2019.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries XAI—explainable artificial intelligence.Science Robotics, 4(37), 2019

Reference 23

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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.

source=pdf_text observed=2026-08-15T19:04:41.242632Z digest=sha256:6aabb644b275a993d6815484e419a03187e918b3f597e08c7bbbbcbc34a415f5

Observation b8e27fb9-ac23-4479-9752-b69a18e13ca0 · outbound

This paper cites Natural Adversarial Examples.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Natural Adversarial Examples

Reference 24

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source=pdf_text observed=2026-08-15T19:04:41.249981Z digest=sha256:e19a5d84022a61968d943c8539cc9ded04242e1944f45838ed40c4830313d7d9

Observation 97f454b0-2a86-4ba4-a3ec-1b3b16a0353e · outbound

This paper cites Unsolved Problems in ML Safety.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Unsolved Problems in ML Safety

Reference 25

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source=pdf_text observed=2026-08-15T19:04:41.259579Z digest=sha256:a47d06276e52018f8066dddec4f6150e2bb7b5ae3ff6568fe7b0ffcfac808872

Observation dc77d8f9-9a32-419a-9860-f46645f8d800 · outbound

This paper cites An Overview of Catastrophic AI Risks.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries An Overview of Catastrophic AI Risks

Reference 26

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source=pdf_text observed=2026-08-15T19:04:41.266452Z digest=sha256:e38e1935eb10ba2f9dc2de7882fa3ca39bb12862d800e2350d660f157a30c74d

Observation 03885cdf-62ea-4b34-9af7-5d1bdb3a61e1 · outbound

This paper cites Chapman and hall/CRC, 2007.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Chapman and hall/CRC, 2007

Reference 27

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.270994Z digest=sha256:3dce15a51da5673dfdbfd21dca226e0eb1cfd3effe7873a8b16555295e7059f4

Observation f28e59ea-0018-4928-b397-8fee8e0a0fa5 · outbound

This paper cites an unresolved cited work.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.279920Z digest=sha256:72ad568dfbf3503b6c9c22445b3f8c5056a87559c4fad7ac8e70884120d3bb54

Observation 513de459-d3f2-4050-a830-fa225c57d3b0 · outbound

This paper cites Experimental security analysis of a modern automobile.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Experimental security analysis of a modern automobile

Reference 29

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T19:04:41.290128Z digest=sha256:10ab1042df58681ca35a6783cdaa7f6b2cdda954290409a885cf6e55c46aeb88

Observation 446e5615-8c0b-4c0b-80ea-4b2721d0d408 · outbound

This paper cites Leveson.Engineering a Safer World: Systems Thinking Applied to Safety.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Leveson.Engineering a Safer World: Systems Thinking Applied to Safety

Reference 30

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source=pdf_text observed=2026-08-15T19:04:41.296281Z digest=sha256:185bc8d15eaa2dc03c27b1c8d3ba72a2b0761afade94457c149b14707104bfcb

Observation ccb6deff-b086-441e-8462-14a812a6ba87 · outbound

This paper cites Lundberg and Su-In Lee.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Lundberg and Su-In Lee

Reference 31

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raw_fallback, observed 2026-08-15T19:04:42.247003Z

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.

source=pdf_text observed=2026-08-15T19:04:41.305162Z digest=sha256:b04daf2e3485267ba4e5624c283d5ec01a849f6eb7a14da74be4834a85367780

Observation 605af2da-4a1d-48e6-a45b-ed8143b83907 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.International Conference on Learning Representations (ICLR), 2018.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Towards deep learning models resistant to adversarial attacks.International Conference on Learning Representations (ICLR), 2018

Reference 32

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raw_fallback, observed 2026-08-15T19:04:42.219070Z

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.

source=pdf_text observed=2026-08-15T19:04:41.311895Z digest=sha256:4af4ef4d6c6200f630d317ee94b01e6aea5e7c4f9b80cd8edac574f61a3c852b

Observation a89a5f91-db8a-4f30-b6b2-613ea3bb1df8 · outbound

This paper cites Vintage Books, 2019.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Vintage Books, 2019

Reference 33

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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.

source=pdf_text observed=2026-08-15T19:04:41.316549Z digest=sha256:1cd60efac7cb736654e6e85797d4430a2431105f25778ee4b6f38aa7ba0cf293

Observation 8deb5827-2e37-4e50-8bed-0bb9ab5988f9 · outbound

This paper cites A survey on bias and fairness in machine learning.ACM Computing Surveys, 54(6):1–35, 2021.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries A survey on bias and fairness in machine learning.ACM Computing Surveys, 54(6):1–35, 2021

Reference 34

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raw_fallback, observed 2026-08-15T19:04:42.175410Z

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.

source=pdf_text observed=2026-08-15T19:04:41.321047Z digest=sha256:1f84140d529044fa87733d3a96758fc19945c165c9b41c060fd960623194492b

Observation 9bffd28b-d9c6-4210-8a50-bcdbe20ae36f · outbound

This paper cites Security and privacy controls for information systems and organizations.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Security and privacy controls for information systems and organizations

Reference 35

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raw_fallback, observed 2026-08-15T19:04:42.158177Z

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.

source=pdf_text observed=2026-08-15T19:04:41.325384Z digest=sha256:29375e69ee143f4a8eaa51839b6e98d83a79721dd692a97c9d4aaa23fb8a5dec

Observation a57e8bea-9195-4ccd-956d-a17566ec2f33 · outbound

This paper cites Artificial intelligence risk management framework (ai rmf 1.0).

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Artificial intelligence risk management framework (ai rmf 1.0)

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:42.133835Z

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.

source=pdf_text observed=2026-08-15T19:04:41.339351Z digest=sha256:2c16b7fcd688556c8d2f1105d0896ae7dbb73fe73851e4056f68ee78cba7c109

Observation 4d1b507b-4935-4ce4-abac-a9cfcb3ac376 · outbound

This paper cites Safely interruptible agents.Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2016.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Safely interruptible agents.Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2016

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:42.114857Z

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.

source=pdf_text observed=2026-08-15T19:04:41.345423Z digest=sha256:ce7b862f24f33787aaf63cbc099896a68a1297660a05abf0119f9622f2105b3c

Observation 915ae77d-0758-4086-bf43-5ef38188a676 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:41.350859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:41.350859Z digest=sha256:5690bcdf8f3c0bf50d934a27efb8a3e7ab587a02ac09c2da33b559719f6f0dab

Observation 69497148-ea8b-4112-93c7-af841641426b · outbound

This paper cites Su, Mengdi Wang, Chaowei Xiao, Bo Li, Dawn Song, Peter Henderson, and Prateek Mittal.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Su, Mengdi Wang, Chaowei Xiao, Bo Li, Dawn Song, Peter Henderson, and Prateek Mittal

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:42.075356Z

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.

source=pdf_text observed=2026-08-15T19:04:41.356296Z digest=sha256:0c303266d851f1782880e4962c28011c5cd09dcd76acb8bc7115f2aed86991b9

Observation 6d30c774-b174-438d-b38d-00b37b4235e3 · outbound

This paper cites why should i trust you?.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries why should i trust you?

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:41.363299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:41.363299Z digest=sha256:08d4f6ce3af23e7ebab3fbfc62e6500dc2c02e861cac7ffca2a04f30ff68b1bc

Observation ae046b76-93e7-41c2-9040-88981842053e · outbound

This paper cites Artificial intelligence and the problem of control.Perspectives on digital humanism, 19:1–322, 2022.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Artificial intelligence and the problem of control.Perspectives on digital humanism, 19:1–322, 2022

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:42.055985Z

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.

source=pdf_text observed=2026-08-15T19:04:41.368181Z digest=sha256:24750cf94dc04ba09abee568a23d56bc8266a4fe45342ebd78e5faf86e6413f4

Observation 73662e88-6dd2-4b94-a01c-9fec7cba5e60 · outbound

This paper cites Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:42.036779Z

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.

source=pdf_text observed=2026-08-15T19:04:41.374689Z digest=sha256:460da689d4f1ed2089c2ab01b84abd02fecdf9b823a291971b8488c1008896f8

Observation 2470675d-9ab8-4516-96aa-b52d095eeb08 · outbound

This paper cites Membership inference attacks against machine learning models.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Membership inference attacks against machine learning models

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.994309Z

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.

source=pdf_text observed=2026-08-15T19:04:41.380135Z digest=sha256:a55657030996c3c74b1f801e89414ec8e0e0b8690f08d3e24f6b0c1dc9a06a54

Observation ead68bb5-9715-4420-9321-0a9af6e4190b · outbound

This paper cites Secure software development framework (ssdf) version 1.1.NIST Special Publication, 800(218):800–218, 2022.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Secure software development framework (ssdf) version 1.1.NIST Special Publication, 800(218):800–218, 2022

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.973023Z

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.

source=pdf_text observed=2026-08-15T19:04:41.384736Z digest=sha256:568144f738f44efe8e5b5c21074975c1098356516d3f283823df0d9ab6fbcf33

Observation 10f069bf-8c3f-4585-b669-d75d0748f191 · outbound

This paper cites Tanenbaum and David J.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Tanenbaum and David J

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.955751Z

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.

source=pdf_text observed=2026-08-15T19:04:41.389961Z digest=sha256:fe4e0ef868e2440085757ef6f63fe1e2520b5774eccec5ddb4d3dad7885fc563

Observation f2496b3e-9241-40de-b221-c1f7aef0faf8 · outbound

This paper cites Ai security has serious terminology issues.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Ai security has serious terminology issues

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.936438Z

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.

source=pdf_text observed=2026-08-15T19:04:41.395342Z digest=sha256:f1d1357c11f7a697f26f59bf0716b3800ff11298b9e0496a47bccf44951b548b

Observation 242da496-ac61-4ac8-b3ad-ad517a28f4a8 · outbound

This paper cites Hachette UK, 2019.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Hachette UK, 2019

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.912445Z

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.

source=pdf_text observed=2026-08-15T19:04:41.399432Z digest=sha256:7e06bb9ff238dd22b3f3d14589ecafe09ea58f2609c6d42a2c4dd69167679977

Observation 7793967c-f8d9-4b29-a169-9dce8954d6d3 · outbound

This paper cites Reiter, and Thomas Ristenpart.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Reiter, and Thomas Ristenpart

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.895610Z

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.

source=pdf_text observed=2026-08-15T19:04:41.403746Z digest=sha256:3fdf1d392e84a04626006520f91632c9a306288e53c01664ee8f0a506a41aac4

Observation 2ae305b5-3c3a-4d48-a41e-5d4e2e3274be · outbound

This paper cites Embedding watermarks into deep neural networks.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Embedding watermarks into deep neural networks

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:41.877217Z

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.

source=pdf_text observed=2026-08-15T19:04:41.408845Z digest=sha256:8b0d262b66752edd67059d898044a93e5f50f6884e3939359f9d05857653708a

Observation 7238465e-761b-4341-9b02-53cdc12e87ef · outbound

This paper cites Ethical and social risks of harm from Language Models.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Ethical and social risks of harm from Language Models

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:41.413640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:41.413640Z digest=sha256:c5aa58bab0b09b0ae393b1f21999e7bf6a3224ae9c1cd60b23b7afb83fd443cd

Observation ba5bcf01-6b62-4c7c-83d2-bb7682c63623 · outbound

This paper cites Improving Alignment and Robustness with Circuit Breakers.

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries Improving Alignment and Robustness with Circuit Breakers

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:41.423735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:41.423735Z digest=sha256:6299c9a9e0b05ffc063c9a8127254b170ccd5cd31350ee52f50e4b63818758e9

Pith citing papers

Observation 72585d12-4fea-4742-a54b-b5b6bae973c5 · inbound

AI Assurance in UK Defence: Challenges in Operationalising JSP 936 cites this paper.

AI Assurance in UK Defence: Challenges in Operationalising JSP 936 AI Safety vs. AI Security: Demystifying the Distinction and Boundaries

Reference 98

Resolution
verified exact
arxiv_id, observed 2026-07-03T03:37:35.434519Z

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.

source=pdf_text observed=2026-06-27T15:11:34.346680Z digest=sha256:dd74be475f0e3717d7340546cf267d1d24f267986b27f0bfc526ee38f4a0e1dc