Paper Citation Record · LEDGER

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2506.19109.

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

pith.paper-citation-record.v1
2506.19109 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:42:19.487526Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T08:17:10.481202Z

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

29 of 29 outbound references displayed

verified exact0
verified fuzzy21
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation b999d55f-6605-4a03-8ce7-fcf8b8cc725e · outbound

This paper cites GitHub - jthack/PIPE: Prompt Injec- tion Primer for Engineers — github.com.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems GitHub - jthack/PIPE: Prompt Injec- tion Primer for Engineers — github.com

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.959818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d99c6c3-b982-46fd-b0d2-ffeabe8e7dd8 · outbound

This paper cites OW ASP Foundation.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems OW ASP Foundation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.943424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 54f1b2d5-3a7a-4580-858b-847859e218db · outbound

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

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applica- tions with Indirect Prompt Injection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.927836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0e101a06-f69a-453f-8ba8-f0be4d41d1a1 · outbound

This paper cites I don’t know how to solve prompt injection — simonwillison.net.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems I don’t know how to solve prompt injection — simonwillison.net

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.912217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 452ee2b2-f4cb-402f-a25e-2143df487753 · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Ignore Previous Prompt: Attack Techniques For Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.369521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 075e0efc-4519-4248-af9e-dedc5cf7d70b · outbound

This paper cites Protect AI.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Protect AI

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.897609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7c6fd8a3-1cbe-4ee4-9c8a-db1377ddd6bd · outbound

This paper cites Release Blog — Vigil: Documentation — vigil.deadbits.ai.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Release Blog — Vigil: Documentation — vigil.deadbits.ai

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.883346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c853418d-5349-4d72-988f-c4d022e80a23 · outbound

This paper cites GitHub - protectai/rebuff: LLM Prompt In- jection Detector — github.com.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems GitHub - protectai/rebuff: LLM Prompt In- jection Detector — github.com

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.868380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd45aae6-f2d7-4926-931b-bcb52829e555 · outbound

This paper cites You can’t solve AI security problems with more AI — simonwillison.net.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems You can’t solve AI security problems with more AI — simonwillison.net

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.854108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7b7b4a38-8dc3-45e5-8968-4091bb1a4a08 · outbound

This paper cites Jailbroken: How Does LLM Safety Training Fail?.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Jailbroken: How Does LLM Safety Training Fail?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.394511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b87d9f74-7675-467c-8b8b-80ad58f345cd · outbound

This paper cites Dont you (forget NLP): Prompt injection with control characters in ChatGPT — dropbox.tech.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Dont you (forget NLP): Prompt injection with control characters in ChatGPT — dropbox.tech

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.839035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7591998-d480-4a87-b609-2bcd9816484a · outbound

This paper cites https://en.wikipe dia.org/wiki/Leet.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems https://en.wikipe dia.org/wiki/Leet

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.824059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7b54bc3-25b7-4122-a10b-d2745714afeb · outbound

This paper cites [redacted for anonymity].

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems [redacted for anonymity]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.807151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 34ef1876-53d3-4cba-96c9-52a47ce055b1 · outbound

This paper cites Fine-Tuned DeBERTa-v3-base for Prompt Injection Detection.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Fine-Tuned DeBERTa-v3-base for Prompt Injection Detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.793071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 27b4a7f3-1c73-4027-81a6-d53eb78c2476 · outbound

This paper cites Prompt Injection Attack on GPT-4 — Robust Intelligence — robustintelligence.com.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Prompt Injection Attack on GPT-4 — Robust Intelligence — robustintelligence.com

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.779080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f81008da-a398-4d68-bccd-75a92e3cae83 · outbound

This paper cites ChatGPT Plugins: Data Exfiltration via Images & Cross Plugin Request Forgery · Embrace The Red — embracethered.com.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems ChatGPT Plugins: Data Exfiltration via Images & Cross Plugin Request Forgery · Embrace The Red — embracethered.com

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.764116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cb355ded-ae7c-4233-83d7-5995150c2e20 · outbound

This paper cites Vector Similarity Explained — Pinecone — pinecone.io.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Vector Similarity Explained — Pinecone — pinecone.io

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.748689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a2b1794c-7a35-4302-875d-d98a2ae75117 · outbound

This paper cites PLeak: Prompt Leaking Attacks against Large Language Model Applications.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems PLeak: Prompt Leaking Attacks against Large Language Model Applications

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.733192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5a81a54-0505-49c3-97f9-1d4785748d28 · outbound

This paper cites Effective Prompt Extraction from Language Models.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Effective Prompt Extraction from Language Models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.715891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c392c87-2a82-4bf8-ae8b-5ef03c7b4f70 · outbound

This paper cites Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.448390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b53a63a3-5c07-43a5-9697-706875fc893f · outbound

This paper cites Raccoon: Prompt Extraction Bench- mark of LLM-Integrated Applications.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Raccoon: Prompt Extraction Bench- mark of LLM-Integrated Applications

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.700232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8edd5c8d-7003-4f2a-acef-0047fee9fba4 · outbound

This paper cites ROUGE: A Package for Automatic Evaluation of Summaries.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems ROUGE: A Package for Automatic Evaluation of Summaries

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.684293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d29e1c0-c783-4438-ac07-183b25e2b388 · outbound

This paper cites 13349–13365.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems 13349–13365

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.457934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02432c12-5bf6-4cfe-a7cd-3ffd225079ac · outbound

This paper cites GitHub - whylabs/langkit: LangKit: An open- source toolkit for monitoring Large Language Models (LLMs).

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems GitHub - whylabs/langkit: LangKit: An open- source toolkit for monitoring Large Language Models (LLMs)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.653981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec22a6ab-2755-4566-acf3-05d8802fb621 · outbound

This paper cites Whispers in the Machine: Confi- dentiality in LLM-integrated Systems.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Whispers in the Machine: Confi- dentiality in LLM-integrated Systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:19.669604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 897a408d-c045-46ae-8910-ee19d7a36a6f · outbound

This paper cites Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.482528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73c4f5f7-ff4e-44b1-b082-3b7c44861f7b · outbound

This paper cites From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.477402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d5060f9-8f28-4e3d-bcf7-f4deda75b119 · outbound

This paper cites Jatmo: Prompt Injection Defense by Task-Specific Finetuning.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Jatmo: Prompt Injection Defense by Task-Specific Finetuning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.487526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce8c889c-987b-4c6c-958d-6addf0340220 · outbound

This paper cites Effective Prompt Extraction from Language Models.

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems Effective Prompt Extraction from Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:19.443665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 50c07733-c7cb-45c1-87a2-3d367f3a7b6b · inbound

Prompt Governance? On Governing Technologies Governed by Natural Language cites this paper.

Prompt Governance? On Governing Technologies Governed by Natural Language Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:25:32.799361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.