Paper Citation Record · LEDGER

Hijacking Large Language Models via Adversarial In-Context Learning

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2311.09948.

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

pith.paper-citation-record.v1
2311.09948 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:31:33.221051Z

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

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
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 14973b43-962e-4911-aedc-d49bf99aeffc · inbound

Towards an AI co-scientist cites this paper.

Towards an AI co-scientist Hijacking Large Language Models via Adversarial In-Context Learning

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:02:44.339484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c46d0d5a-1d65-40fa-b1f0-f571846445f8 · inbound

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

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Hijacking Large Language Models via Adversarial In-Context Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:33.221051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef79adea-ac4a-450d-b779-5d9e179f914d · inbound

How Can Mamba Learn In Context with Outliers and Generalize Provably? cites this paper.

How Can Mamba Learn In Context with Outliers and Generalize Provably? Hijacking Large Language Models via Adversarial In-Context Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T13:29:31.557755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f084c20-4669-487d-9c81-b122f500b841 · inbound

When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents cites this paper.

When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents Hijacking Large Language Models via Adversarial In-Context Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T15:14:13.376775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7f40aed0-e9e2-42d2-a0a5-88bce6623002 · inbound

When AI reviews science: Can we trust the referee? cites this paper.

When AI reviews science: Can we trust the referee? Hijacking Large Language Models via Adversarial In-Context Learning

Reference 108

Resolution: verified exact; arxiv_id, observed 2026-05-08T23:19:29.999422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 047b4222-96f8-4a2d-9720-97995c22b443 · inbound

On the Hardness of Junking LLMs cites this paper.

On the Hardness of Junking LLMs Hijacking Large Language Models via Adversarial In-Context Learning

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:21:10.127581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 48ed8345-50d9-4ab7-8d1a-e741a4e5a888 · inbound

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations cites this paper.

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations Hijacking Large Language Models via Adversarial In-Context Learning

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:26.707805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1274c455-a1b4-45f2-bb65-7df0fedbe888 · inbound

When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning cites this paper.

When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning Hijacking Large Language Models via Adversarial In-Context Learning

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:34:02.714544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.