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Paper Citation Record · LEDGER

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security

As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2607.18063.

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

pith.paper-citation-record.v1
2607.18063 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:19:08.659588Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved11
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb9d54ac-a36d-49d8-8a77-d270fe776526 · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.591855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.591855Z digest=sha256:f4ba711c3c34f9b0d3f1c65fef723103609e5b0bfaafc6b1ab0790a285e71ebe

Observation 10f70f7c-61ec-4297-bb8c-aa8255be8e44 · outbound

This paper cites an unresolved cited work.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Unresolved cited work

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.632076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.632076Z digest=sha256:dcbe04e51e7e2fcfc3d12c9364be42908bc51014e3a09f2404ab5839a28e5b61

Observation d47b433a-ecdd-4c60-bf5f-c86b2e713985 · outbound

This paper cites Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.637388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.637388Z digest=sha256:a8ccee110d70ef2b1a79d053902ffaaf0d63ae4b9814a07c59f82d4b78f9452b

Observation bb1f7e20-d6c8-44c1-b2c6-e26358957c51 · outbound

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

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.642230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.642230Z digest=sha256:694bfba0c09058ee4ef6ec5ef26ffa9e51da9516e895fe13a1f3ceb2b07e4090

Observation 87315c15-adac-4ece-8b2c-ae5182aef1ee · outbound

This paper cites static scanner false positive.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security static scanner false positive

Reference 11

Resolution
malformed identifier
no resolver link, observed 2026-08-01T16:19:08.646941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.646941Z digest=sha256:c79586c1e8dc49f196b226d069a3decd228a93e8f9ec53f23aa15537bcec8a7a

Observation 88f25e9e-539c-4864-9000-f8e8b018bb8f · outbound

This paper cites metadata.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security metadata

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.659588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.659588Z digest=sha256:69e7e832504148e9236069b5f5609bd875cc0de1f96884da27b5d0a2eceb9849

Observation 2543fac7-c219-41b2-8216-1b93274a14d9 · outbound

This paper cites discrimination.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security discrimination

Reference 46

Resolution
malformed identifier
no resolver link, observed 2026-08-01T16:19:08.653322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.653322Z digest=sha256:992097fbe66a07b2d6a0f861c1515f10f7ef0173ec03be920a8b9e3b20d20e8e

Observation 52be3610-9d22-4093-b472-d455bb546b5d · outbound

This paper cites LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet

Reference 1939

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.626130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.626130Z digest=sha256:7aa548cf0e9524497189f1842c420e93c51ed64d643ab38d64840c366695dd88

Observation a980366c-883e-4df2-b65b-529546b13abb · outbound

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

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Reference 2000

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.620825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.620825Z digest=sha256:3c1ecdbb4b7025bf40ade0e36da4de9dcefce3839b35f5e43af2c29f2b67dfd1

Observation 3e85ae6c-d35b-43d4-a19d-aa88b69d0520 · outbound

This paper cites Pappas, Florian Tramèr, Hamed Hassani, and Eric Wong.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Pappas, Florian Tramèr, Hamed Hassani, and Eric Wong

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.609960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.609960Z digest=sha256:21b1d245863ba008512568ef3b0a1bba6348f292fd254b0a290a82d05e193fcb

Observation 20af0eb7-595f-4044-ac48-05c1df91ff6a · outbound

This paper cites AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.615174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.615174Z digest=sha256:1c792cf0964312f524ea60a832f069554eb4d385bac2a71db334377d407b4dc7

Observation 4128cf2e-4331-435a-ac9f-e32c8e1046d9 · outbound

This paper cites Breaking agent backbones: Evaluating the security of backbone LLMs in AI agents.arXiv preprint arXiv:2510.22620,.

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Breaking agent backbones: Evaluating the security of backbone LLMs in AI agents.arXiv preprint arXiv:2510.22620,

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.598377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.598377Z digest=sha256:45a668a9e3dd7116230093440c17d3634cb71625da2314fb626a027106e61f6d

Observation 4b301f0e-cb13-4c73-b675-e3ed922cd45a · outbound

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

Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 2026

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:08.603833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:19:08.603833Z digest=sha256:6c116330a825ca8f659d860fb7f18aa1cf83eac08a5e3c427a3ec568c309280b

Pith citing papers

No inbound Pith citation observations are available.