Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.13926.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:59:27.341646Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T19:59:40.087446Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 84c06060-d3f9-4253-95f8-4c43f4a49692 · inbound
A Technical Survey of Reinforcement Learning Techniques for Large Language Models cites this paper.
A Technical Survey of Reinforcement Learning Techniques for Large Language Models Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:59:40.141562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 588c10bd-6657-46d9-8169-db93e98a0fa8 · inbound
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM cites this paper.
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:13:00.922194Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db9352be-3e94-4c12-a714-e05670708b7b · inbound
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:31:39.106420Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29c1cd60-2446-4ef3-bb2a-8383c246b32c · inbound
Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild" cites this paper.
Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild" Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:03:34.617279Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fdbb7c73-db57-4a6b-9d2e-f06c0f95da81 · inbound
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:46:12.796894Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.