Paper Citation Record · LEDGER

SPML: A DSL for Defending Language Models Against Prompt Attacks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.11755.

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

pith.paper-citation-record.v1
2402.11755 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:12:26.950931Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:42:33.713418Z

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 1d905b6b-076f-4ac4-8612-f685ce262123 · inbound

Jailbreak Attacks and Defenses Against Large Language Models: A Survey cites this paper.

Jailbreak Attacks and Defenses Against Large Language Models: A Survey SPML: A DSL for Defending Language Models Against Prompt Attacks

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T02:20:44.814889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eb408165-2f4c-4324-9a7f-f7a7be7ae53d · inbound

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety cites this paper.

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety SPML: A DSL for Defending Language Models Against Prompt Attacks

Reference 138

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:42:33.716458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fc2934dd-6fa1-4bd2-b89f-6951d06ad973 · inbound

What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs cites this paper.

What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs SPML: A DSL for Defending Language Models Against Prompt Attacks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:12:26.950931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation def551ae-e4a9-4543-b914-9b4540ef0036 · inbound

ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments cites this paper.

ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments SPML: A DSL for Defending Language Models Against Prompt Attacks

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:02:54.833255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e984a50b-2dfc-4d54-ab0a-fa2c943e4b45 · inbound

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

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation SPML: A DSL for Defending Language Models Against Prompt Attacks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:31.765489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 886a7d60-906a-4714-a154-b0c6406cbc29 · inbound

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses cites this paper.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses SPML: A DSL for Defending Language Models Against Prompt Attacks

Reference 159

Resolution: unresolved; no resolver link, observed 2026-08-04T09:25:53.030923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.