Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2403.12503.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T22:01:05.751152Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T11:23:20.684047Z
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 1f1d28e6-5439-4257-98d4-b0970eb689c9 · inbound
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models cites this paper.
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T18:23:05.685950Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9520465-5aab-460b-a14c-e358d7f1fbc4 · inbound
SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment cites this paper.
SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:01:05.751152Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6490be7d-41d4-47ae-adc0-6c4050d28471 · inbound
A Byzantine Fault Tolerance Approach towards AI Safety cites this paper.
A Byzantine Fault Tolerance Approach towards AI Safety Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T19:16:58.761112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5d90c981-39c5-4f78-8778-f460e9c022ad · inbound
Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystem cites this paper.
Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystem Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:09:49.761576Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e9f03677-1337-4149-808e-22d3de271ec2 · inbound
Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding cites this paper.
Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:54:43.037974Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 075391b3-1e02-4f61-8461-efaa4094e5cf · 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 Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:46:11.329574Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e41ce24d-c652-4fb6-9257-ca7f208a029a · inbound
A First Look at the Security Issues in the Model Context Protocol Ecosystem cites this paper.
A First Look at the Security Issues in the Model Context Protocol Ecosystem Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T06:12:26.325498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ec1c7a04-3e2e-4b8d-a00a-579ff950915a · inbound
Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities cites this paper.
Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T11:23:20.685524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 48ab9af9-a1af-4b86-b4b7-8acda2639b5b · inbound
Adversarial Prompting Framework for AI Safety Assessment cites this paper.
Adversarial Prompting Framework for AI Safety Assessment Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T05:10:09.277660Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.