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Paper Citation Record · LEDGER

Stealing Part of a Production Language Model

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 43 inbound Pith citation observations for arXiv:2403.06634.

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

pith.paper-citation-record.v1
2403.06634 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 43 of 43 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 43 of 43 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:37:23.923474Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

7
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 324200b2-0390-47cc-a1a0-5a19ca5ab790 · inbound

Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents cites this paper.

Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents Stealing Part of a Production Language Model

Reference 35

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no resolver link, observed 2026-08-12T20:36:01.431273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:36:01.431273Z digest=sha256:9037fa03414b02903debe382d59808f72c268c0a599c9df1c2e8f258d21d71df

Observation c9f6403f-12c6-4879-a935-d77cd372941d · inbound

Does Prompt Formatting Have Any Impact on LLM Performance? cites this paper.

Does Prompt Formatting Have Any Impact on LLM Performance? Stealing Part of a Production Language Model

Reference 5

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no resolver link, observed 2026-08-12T19:38:26.367067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T19:38:26.367067Z digest=sha256:4fca7f4aa18b2e2cc269387fcd4fa06201fee3e337a75feb9063538c83e4e289

Observation 759ef61a-2a82-4dc0-a07b-7d583a0bdf4a · inbound

AI Safety Frameworks Should Include Procedures for Model Access Decisions cites this paper.

AI Safety Frameworks Should Include Procedures for Model Access Decisions Stealing Part of a Production Language Model

Reference 24

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no resolver link, observed 2026-08-12T19:37:34.866373Z

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source=pdf_text observed=2026-08-12T19:37:34.866373Z digest=sha256:6e26a9ba56f9cddb125516c83bdaf6b6b3048dc8b19387dea5e51bef34ae8a05

Observation c546f038-1618-45cb-84fd-3bc79161a0be · inbound

Position Paper: Model Access should be a Key Concern in AI Governance cites this paper.

Position Paper: Model Access should be a Key Concern in AI Governance Stealing Part of a Production Language Model

Reference 52

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no resolver link, observed 2026-08-12T05:00:04.307866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:00:04.307866Z digest=sha256:3b0944cda8e13535c806346f59482376b5f5a5302e9e2fd2428b70de104698a7

Observation 1f9a7a37-d4ad-43c8-a9e1-3f578c733ef6 · inbound

LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage cites this paper.

LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage Stealing Part of a Production Language Model

Reference 2024

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no resolver link, observed 2026-08-11T20:30:10.200969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:30:10.200969Z digest=sha256:8317ad74c719f0ea9252284e4dcb460554b54e32da37c05d344cb404c6e28527

Observation 50598d84-6766-4d02-bba8-4313abe9643e · inbound

Queries, Representation & Detection: The Next 100 Model Fingerprinting Schemes cites this paper.

Queries, Representation & Detection: The Next 100 Model Fingerprinting Schemes Stealing Part of a Production Language Model

Reference 6

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no resolver link, observed 2026-08-11T13:37:58.709784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:37:58.709784Z digest=sha256:f3846a6240f8e17c7e6ff693cf61fa9338b9c753b780c333fbe46956e93b204d

Observation 21199f8f-cd14-411e-b796-7f97485b69b4 · inbound

Rerouting LLM Routers cites this paper.

Rerouting LLM Routers Stealing Part of a Production Language Model

Reference 18

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no resolver link, observed 2026-08-10T22:24:45.431429Z

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source=pdf_text observed=2026-08-10T22:24:45.431429Z digest=sha256:d5205c1d1e7c1774c2c674c054966a34ffd4d3940a0c2ef8b5f974402c571690

Observation 5e7e3a1c-59f7-475f-a08d-89bca95f164c · inbound

Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface cites this paper.

Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface Stealing Part of a Production Language Model

Reference 28

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no resolver link, observed 2026-08-10T19:44:46.751961Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:44:46.751961Z digest=sha256:b221986ddfe8656dc4362e9de4224f72fe916a990ee7d95703c002eb86e57256

Observation 62b9262a-9a08-4344-ae83-fb910a0907e2 · inbound

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs cites this paper.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Stealing Part of a Production Language Model

Reference 5

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no resolver link, observed 2026-08-09T20:51:49.931701Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T20:51:49.931701Z digest=sha256:6bb143066440c35a84a4043caf0fc16684b4071e4bbde4090275ce80d8266de9

Observation 95873777-caaa-4f50-8c22-974f7cd15bb1 · inbound

Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights cites this paper.

Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights Stealing Part of a Production Language Model

Reference 4

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no resolver link, observed 2026-08-07T20:53:28.387219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T20:53:28.387219Z digest=sha256:b7e43966c4e564ce136c159ff903e540994e2da706bff674b00263c4d26134d9

Observation 3ad216d6-77da-4380-99ad-2b5e500eeafd · inbound

Safety Co-Option and Compromised National Security: The Self-Fulfilling Prophecy of Weakened AI Risk Thresholds cites this paper.

Safety Co-Option and Compromised National Security: The Self-Fulfilling Prophecy of Weakened AI Risk Thresholds Stealing Part of a Production Language Model

Reference 20

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no resolver link, observed 2026-08-16T11:37:23.923474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:37:23.923474Z digest=sha256:9c1c82f8a344095da4630302e376ab938fe6662e8be92c6fdaae5a0a4ead7ca9

Observation 53716b10-733f-4ed7-8eeb-e954633f1122 · inbound

Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs cites this paper.

Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs Stealing Part of a Production Language Model

Reference 38

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no resolver link, observed 2026-08-15T23:23:39.833907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:23:39.833907Z digest=sha256:969148212610845e588a8e4b812dd080fd569efb25a5563f8fdcf41f9124ba6a

Observation 55c0d075-6f24-4aaa-af3b-a0fc6343dfa6 · inbound

Opening the Scope of Openness in AI cites this paper.

Opening the Scope of Openness in AI Stealing Part of a Production Language Model

Reference 2024

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no resolver link, observed 2026-08-15T22:45:02.187478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:45:02.187478Z digest=sha256:ced14c5add41bb4b134a908338ad55460c17d1efccfe6b5ba24d2b787859ae42

Observation 73e0720e-0a6e-4fac-8b0b-2be1b247db27 · inbound

Fragments to Facts: Partial-Information Fragment Inference from LLMs cites this paper.

Fragments to Facts: Partial-Information Fragment Inference from LLMs Stealing Part of a Production Language Model

Reference 7

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no resolver link, observed 2026-08-15T20:16:09.316325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:16:09.316325Z digest=sha256:e9594c38f9f4e2b1b7b68646d59268e4e8aa880e2b69efcd208048f4b9b4ae62

Observation 1e05a6e3-11f7-41e4-af7b-2d666af5c970 · inbound

Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services cites this paper.

Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services Stealing Part of a Production Language Model

Reference 4

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no resolver link, observed 2026-08-07T14:34:34.779319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:34.779319Z digest=sha256:b8e9c649fd8365c2a4407ce05928381f44cf7dfab2860327f0796ed9f8f4a9a2

Observation 76c89e6d-1f98-451e-ba59-bb9b2433b228 · inbound

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects cites this paper.

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects Stealing Part of a Production Language Model

Reference 20

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no resolver link, observed 2026-08-07T14:26:34.918711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:26:34.918711Z digest=sha256:d3e4011faa93b1bdc3e22b87d4ee97f4cc63e2425224d2ef1c7a54b63b818df6

Observation 381bac5a-b44b-42a5-9686-fd745ce10bd0 · inbound

Approximating Language Model Training Data from Weights cites this paper.

Approximating Language Model Training Data from Weights Stealing Part of a Production Language Model

Reference 8

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no resolver link, observed 2026-08-06T23:58:58.659790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:58:58.659790Z digest=sha256:9b49a6225936ee75f1c5c95388af5457b8355826946006db557c03fe191fcda4

Observation 98a5c5b6-44fb-4307-84e4-ad3e27534878 · inbound

Report on NSF Workshop on Science of Safe AI cites this paper.

Report on NSF Workshop on Science of Safe AI Stealing Part of a Production Language Model

Reference 3

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no resolver link, observed 2026-08-06T23:01:43.167408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:01:43.167408Z digest=sha256:3cbda00e8a8b18dc297cae3053ddd0c574cd8b312c0c37bb445d25abc165a115

Observation 595b52f3-0a8b-46de-9fde-a94212a96ecb · inbound

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

A Survey on Model Extraction Attacks and Defenses for Large Language Models Stealing Part of a Production Language Model

Reference 5

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no resolver link, observed 2026-08-06T22:23:07.379249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:23:07.379249Z digest=sha256:62ff580ae991d8a4ff3a81a064c36166884c9ac4550f17e993017bdf19de24e9

Observation 18b7d635-0fdc-43d2-afeb-2ad65315a2bb · inbound

Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments cites this paper.

Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments Stealing Part of a Production Language Model

Reference 12

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no resolver link, observed 2026-08-06T21:45:00.538286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:45:00.538286Z digest=sha256:10819dd16e1ab567f53d752ba5ee9ab9af84ccc103733f0d6cb80321cbc89405

Observation e5178f43-11bb-42cb-9e4c-c56a29797357 · inbound

LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users cites this paper.

LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users Stealing Part of a Production Language Model

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T06:02:07.798930Z

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=2026-05-19T05:58:17.452837Z digest=sha256:bf7539202b4117863ea937ffae6b6ae32d6952a3326aa52f58eea9a1f8e7d13a

Observation 1b0a2967-6868-4495-8b81-46222b80ce65 · inbound

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests cites this paper.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Stealing Part of a Production Language Model

Reference 10

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no resolver link, observed 2026-08-06T17:21:33.552558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:21:33.552558Z digest=sha256:a5d0ebb03e44efcda72747e0202fa44da32c0f667bf4705d91c41b75c8c8fa34

Observation d6cc971b-6990-4a4d-bb36-32a0697f54b7 · inbound

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals cites this paper.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Stealing Part of a Production Language Model

Reference 7

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no resolver link, observed 2026-08-06T14:22:38.225032Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:22:38.225032Z digest=sha256:ae2d44d5a13fa5f69642861ec63943b3ecd940a3c4f86035cb76725e85cdd3b2

Observation 8ae517b6-acfb-4e47-a9c9-c69450885e54 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Stealing Part of a Production Language Model

Reference 117

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no resolver link, observed 2026-08-05T20:31:43.751972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:31:43.751972Z digest=sha256:0df77bd52aa820e74d5d34ae8cbdcebe43e1104dcd44f5715f10e8a1d3a59ebd

Observation 8b0fc8b7-9127-4251-8ab1-858a626df843 · inbound

Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous cites this paper.

Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous Stealing Part of a Production Language Model

Reference 7

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no resolver link, observed 2026-08-05T19:39:09.558565Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:39:09.558565Z digest=sha256:5528151d7b6112866ccc3e2ab740b23dc0253d981cbb46099bce4426430ed837

Observation fb9fe567-86a7-4880-ac39-825b1a7a7b1f · inbound

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives cites this paper.

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives Stealing Part of a Production Language Model

Reference 29

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no resolver link, observed 2026-08-05T18:12:36.156283Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T18:12:36.156283Z digest=sha256:975bebbdb2094cb551e1fa94c9ac78db8002d9409e8b79434279a302f53959bb

Observation 7f7ae2e8-8747-41c1-babe-7158987e99cc · inbound

Fingerprinting LLMs via Prompt Injection cites this paper.

Fingerprinting LLMs via Prompt Injection Stealing Part of a Production Language Model

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-21T21:30:39.350060Z

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=2026-05-21T21:29:26.153307Z digest=sha256:7153cefd4b8d34697c4ed576f94eb9416619f449ae26714aeac68aa5b2c5699d

Observation 3f63330e-4d56-4554-83f7-a46df38057f5 · inbound

CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation cites this paper.

CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation Stealing Part of a Production Language Model

Reference 15

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no resolver link, observed 2026-08-03T20:28:51.160758Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:28:51.160758Z digest=sha256:a23a1b4e59e999a76aaa8e3653e0da8c234b576578df4c7b6e9fbd782366ba2b

Observation c64fe9a5-af37-4d5a-af0c-d61d304bbd01 · 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 Stealing Part of a Production Language Model

Reference 41

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T13:15:31.225992Z digest=sha256:600349757e85117b6c67d89cf2f124e680f66e307010a40cc41f01dc24cbc31f

Observation 95a0a283-8970-463a-a036-3aa3001dabfc · inbound

Characterizing Linear Alignment Across Language Models cites this paper.

Characterizing Linear Alignment Across Language Models Stealing Part of a Production Language Model

Reference 1

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unresolved
no resolver link, observed 2026-07-13T22:20:23.676745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T22:20:23.676745Z digest=sha256:21d80f0eebf06b81c52f1427a81327ef55401f861057abbee29de269104570d0

Observation 58a334a5-cd02-4c1b-8a6a-72c40a4a69f2 · inbound

A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework cites this paper.

A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework Stealing Part of a Production Language Model

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:51:09.728099Z

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=2026-05-08T07:53:13.746141Z digest=sha256:0f70d3ba18c0f93e9917b22db00950ed299cf4870d57a1c2791fb0a2e8d4b14f

Observation e00a2d22-2289-476c-9e5a-3f71f23555fe · inbound

On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference cites this paper.

On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference Stealing Part of a Production Language Model

Reference 163

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verified exact
arxiv_id, observed 2026-05-11T17:36:06.501167Z

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=arxiv_source observed=2026-05-08T17:24:04.123827Z digest=sha256:7fd526e1043dbaa695660f5f707cd92940e8d10c5d61899417ab45bd3497f00b

Observation c310ef3d-748e-4d87-8fcf-70172becfe8a · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Stealing Part of a Production Language Model

Reference 85

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metadata mismatch
arxiv_id, observed 2026-05-13T05:57:21.574789Z

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=arxiv_source observed=2026-05-13T05:56:38.042978Z digest=sha256:335cf4abd50401fc31d8c7efbe7d675514554bd66602765039ca09487b654f71

Observation 3a8d368c-665a-4992-9445-d2efd5c3034a · inbound

The Surface You Test Is Not the Surface That Breaks cites this paper.

The Surface You Test Is Not the Surface That Breaks Stealing Part of a Production Language Model

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:33:31.196589Z

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=arxiv_source observed=2026-06-29T06:37:19.674012Z digest=sha256:b2787c59939e8e8369fd39430c8b232e8458a151081e769e7026adb13dfcc69d

Observation 6e1f4a1c-6ff8-432b-9b5f-226f5cf5d927 · inbound

The Geometry of Last-Layer Model Stealing cites this paper.

The Geometry of Last-Layer Model Stealing Stealing Part of a Production Language Model

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-02T16:27:09.535940Z

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=2026-06-27T22:38:51.570323Z digest=sha256:6e88dd1d7c8fb92c9d22f85f8f4331019b884c1a962cbb7ae1d122848792ce5d

Observation 94efd86d-ba91-4802-af11-79be1c394e34 · inbound

SoK: Colluding Adversaries in Machine Learning Pipelines cites this paper.

SoK: Colluding Adversaries in Machine Learning Pipelines Stealing Part of a Production Language Model

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-07-03T02:07:33.530859Z

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=2026-06-27T16:10:51.471822Z digest=sha256:9269c1f03f6a4f80ca85b7d903dd2a163ce98a9bbd14a17dfe63181b7f5f9d63

Observation bf19d2af-e8e1-4d36-a17c-4fc5c33f01bb · inbound

OTRO: Oblivious Tokenization Path with Square-Root ORAM cites this paper.

OTRO: Oblivious Tokenization Path with Square-Root ORAM Stealing Part of a Production Language Model

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:28:49.722869Z

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=2026-06-27T02:53:23.964034Z digest=sha256:dae450ae800de8b3a9c2c067db5938525946caecc402efc839c1a8295b96bacc

Observation 83fcb56b-12d2-446f-92ad-13bf22621414 · inbound

Channel Location Constrains the Auditability of Subliminal Learning cites this paper.

Channel Location Constrains the Auditability of Subliminal Learning Stealing Part of a Production Language Model

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:19:44.429061Z

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=2026-06-26T11:52:03.948568Z digest=sha256:1bf176c64f81856cc6c8b579dce772a141b8a9a6c82cc03268e3a1c415a7e874

Observation 59e7817f-0402-4097-a72f-42a48e7716a3 · inbound

Surrogate Fidelity: When Can Open LLMs Explain Closed Ones? cites this paper.

Surrogate Fidelity: When Can Open LLMs Explain Closed Ones? Stealing Part of a Production Language Model

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-07-01T09:45:40.067713Z

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=arxiv_source observed=2026-07-01T06:15:42.011563Z digest=sha256:3fdae7882f90ef61a1bdea52e28b3c4bb0a5c5e86fd430aa85711fba79297a86

Observation d6d491e5-9e09-4ed9-9c0a-f9c281da15ac · inbound

Black-Box Inference of LLM Architectural Properties with Restrictive API Access cites this paper.

Black-Box Inference of LLM Architectural Properties with Restrictive API Access Stealing Part of a Production Language Model

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-07-03T21:38:58.167809Z

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=arxiv_source observed=2026-07-03T21:34:24.902867Z digest=sha256:2d5586d7d9f95c923eff506b5da2b74213ae73f72f763e3910c35f5c2cc91154

Observation 3c42abf6-f1ef-4a00-b1dc-878cc251cc65 · inbound

Can Watermarking Techniques Help Prevent LLM Model Stealing? cites this paper.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Stealing Part of a Production Language Model

Reference 61

Resolution
unresolved
no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:a4413b9fb4838b7ffb6b93bec5e3b6e3226eb7fd015a00113073f16027179402

Observation d1692e84-1271-44fa-bb01-77d0f6edbccd · inbound

Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions cites this paper.

Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions Stealing Part of a Production Language Model

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T02:34:08.606939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:34:08.606939Z digest=sha256:032c6aea2756e92edafde030669b341eba4efd3d6c41c9a8ac0177aa1809ec79

Observation 13dc8bbc-f492-4229-9cb5-4ada2d9b8f04 · inbound

Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions cites this paper.

Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions Stealing Part of a Production Language Model

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-04T01:45:18.338994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:45:18.338994Z digest=sha256:ba36c0f9b63a3928591d7fdee639048a98b49358c96d3a9196cc63628dd4a457