Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset | Paper Citation Record

Observation f0e68921-4f4a-47b1-aeb6-b3c2eceb3da7 · outbound

This paper cites https://github.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://github.com

Reference 1

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Observation ff41f1ee-e35e-47f9-82f5-865f626bb83e · outbound

This paper cites https://scholar.google.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://scholar.google.com

Reference 2

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Observation 506c55bc-6720-4b74-a9e8-dd8cca9a14b6 · outbound

This paper cites https://simonwillison.net/ 2024/Mar/5/prompt-injection-jailbreaking/.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://simonwillison.net/ 2024/Mar/5/prompt-injection-jailbreaking/

Reference 3

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Observation f1b74ef1-9cbb-4057-8d2d-7310599681e9 · outbound

This paper cites https://www.reddit.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://www.reddit.com

Reference 4

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Observation d95e6bcb-0e3e-4149-8e1e-430618568af4 · outbound

This paper cites https://www.pinecone.io/.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://www.pinecone.io/

Reference 5

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Observation 88da3394-eb1b-48cf-9a70-d033879f2d85 · outbound

This paper cites https://twitter.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://twitter.com

Reference 6

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Observation 2c1e1603-0ef0-419b-a4a7-67e92de01b42 · outbound

This paper cites https://learnprompting.org/docs/prompt_hacking/defensive_measures/ sandwich_defense, 2023.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset https://learnprompting.org/docs/prompt_hacking/defensive_measures/ sandwich_defense, 2023

Reference 7

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Observation eaf36aa9-e5ac-4ad7-a07c-f3ea0fa76fd5 · outbound

This paper cites Conversational Health Agents: A Personalized LLM-Powered Agent Framework.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Conversational Health Agents: A Personalized LLM-Powered Agent Framework

Reference 8

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Observation 8f84fc33-daf6-4528-8345-e0a70c8e3fb2 · outbound

This paper cites GPT-4 Technical Report.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset GPT-4 Technical Report

Reference 9

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Observation cf085b48-a2c5-4ea4-95d9-5d6671479d72 · outbound

This paper cites Vulnerabilities in personal firewalls caused by poor security usability.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Vulnerabilities in personal firewalls caused by poor security usability

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.904279Z

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Observation 80546af0-e341-4d9b-8a9b-4e66041a4f83 · outbound

This paper cites Real Attackers Don’t Compute Gradients.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Real Attackers Don’t Compute Gradients

Reference 11

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Observation 04706def-103c-49fe-b6b6-77a9424e9edf · outbound

This paper cites Protection — arthur.ai.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Protection — arthur.ai

Reference 12

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Observation 6efeb713-d3be-49a9-9a13-b296b1d97836 · outbound

This paper cites LLM Hacking: Prompt Injection Techniques, July 2023.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset LLM Hacking: Prompt Injection Techniques, July 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.858584Z

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Observation b6c56af9-ff06-4f3f-8eb3-65330472375e · outbound

This paper cites an unresolved cited work.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Unresolved cited work

Reference 14

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Observation cdb415f0-7282-4878-8ee1-c1aec9cb798e · outbound

This paper cites A LLM Assisted Exploitation of AI-Guardian.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset A LLM Assisted Exploitation of AI-Guardian

Reference 15

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This paper cites Vigil | Vigil: Documentation — vigil.deadbits.ai.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Vigil | Vigil: Documentation — vigil.deadbits.ai

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.829099Z

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Observation 58b77020-31f3-4274-81df-645ef39e9b74 · outbound

This paper cites Gemini by google deepmind.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Gemini by google deepmind

Reference 17

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Observation 6b37db8c-8a8d-4b6e-ab4a-ed126fde8692 · outbound

This paper cites deepset/prompt-injections · Datasets at Hugging Face — huggingface.co.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset deepset/prompt-injections · Datasets at Hugging Face — huggingface.co

Reference 18

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Observation 098e3a63-5cb1-446a-9453-463d75a855c4 · outbound

This paper cites garak: A Framework for Security Probing Large Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset garak: A Framework for Security Probing Large Language Models

Reference 19

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Observation 61b62d46-6cf1-4df7-ae09-760cc699a3fd · outbound

This paper cites Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.783825Z

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Observation 8f4d7a34-5247-4e5a-844c-79799588b742 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.769269Z

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Observation e3686536-5993-48ee-99e9-98ef658a887d · outbound

This paper cites How should pre-trained language models be fine-tuned towards adversarial robustness? Advances in Neural Information Processing Systems , 34:4356–4369, 2021.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset How should pre-trained language models be fine-tuned towards adversarial robustness? Advances in Neural Information Processing Systems , 34:4356–4369, 2021

Reference 22

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Observation 7cb389fe-4499-4f77-9591-03ed00b92185 · outbound

This paper cites Comparing sql injection detection tools using attack injection: An experimental study.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Comparing sql injection detection tools using attack injection: An experimental study

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.739108Z

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Observation 8eaa1f8e-fa87-44b1-a5fb-58743a8c6d11 · outbound

This paper cites Comparing sql injection detection tools using attack injection: An experimental study.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Comparing sql injection detection tools using attack injection: An experimental study

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.724329Z

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This paper cites Testing and comparing web vulnerability scanning tools for sql injection and xss attacks.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Testing and comparing web vulnerability scanning tools for sql injection and xss attacks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.709807Z

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Observation 4bce2125-6ed5-4d9c-9389-902bbc19b337 · outbound

This paper cites Testing and comparing web vulnerability scanning tools for sql injection and xss attacks.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Testing and comparing web vulnerability scanning tools for sql injection and xss attacks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.694178Z

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This paper cites Challenges in the real world use of classification accuracy metrics: From recall and precision to the matthews correlation coefficient.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Challenges in the real world use of classification accuracy metrics: From recall and precision to the matthews correlation coefficient

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.679172Z

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Observation ae3f784f-3019-45a4-8d6b-ddf275e4a2e4 · outbound

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Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Github copilot

Reference 28

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

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Reference 29

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This paper cites Benchmarking approach to compare web applications static analysis tools detecting owasp top ten security vulnerabilities.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Benchmarking approach to compare web applications static analysis tools detecting owasp top ten security vulnerabilities

Reference 30

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Observation 87921a93-0d57-46bf-b4b0-a6b835e605d9 · outbound

This paper cites Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming

Reference 31

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This paper cites Protect your AI applications in real time — Robust Intelligence — robustintelligence.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Protect your AI applications in real time — Robust Intelligence — robustintelligence.com

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.632889Z

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This paper cites Chatgpt for good? on opportunities and challenges of large language models for education.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Chatgpt for good? on opportunities and challenges of large language models for education

Reference 33

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Observation 7b2e34c1-dc75-4987-b751-573afca309a3 · outbound

This paper cites Software updates as a security metric: Passive identification of update trends and effect on machine infection.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Software updates as a security metric: Passive identification of update trends and effect on machine infection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.601443Z

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This paper cites i have no idea what i’m doing.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset i have no idea what i’m doing

Reference 35

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Observation 93f57253-dd56-4c52-8c37-9055f6ccd3ad · outbound

This paper cites Watch Your Language: Investigating Content Moderation with Large Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Watch Your Language: Investigating Content Moderation with Large Language Models

Reference 36

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This paper cites Cummings, and Alexander Stimpson.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Cummings, and Alexander Stimpson

Reference 37

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Observation 353d97cb-3177-4ce1-98c5-0db2332cb9df · outbound

This paper cites Weight Poisoning Attacks on Pre-trained Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Weight Poisoning Attacks on Pre-trained Models

Reference 38

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Observation 45a86277-98c9-4d99-8278-1cfea7ed5c40 · outbound

This paper cites 12 Top LLM Security Tools: Paid & Free (Overview) | Lakera – Protecting AI teams that disrupt the world.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset 12 Top LLM Security Tools: Paid & Free (Overview) | Lakera – Protecting AI teams that disrupt the world

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.556531Z

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Observation e56f4db5-1a9b-4342-b8ae-1e8ac4f22d1e · outbound

This paper cites End-to-End Security for the Generative AI Era — lasso.security.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset End-to-End Security for the Generative AI Era — lasso.security

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.541653Z

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 5885f206-6323-4020-a185-0d4714f53b49 · outbound

This paper cites Learn Prompting: Your Guide to Communicating with AI.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Learn Prompting: Your Guide to Communicating with AI

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.525538Z

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 03cec968-6942-430f-9e31-50fa9e878515 · outbound

This paper cites AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.444246Z

Source-reported events for the cited work

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Observation 674dc49f-3314-449d-b700-e43f0bed3fd8 · outbound

This paper cites Formalizing and benchmarking prompt injection attacks and defenses, 2024.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Formalizing and benchmarking prompt injection attacks and defenses, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.494354Z

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Observation 628356a8-1e50-4ab9-af8b-921a2624327d · outbound

This paper cites Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.478878Z

Source-reported events for the cited work

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Observation 2e9a912e-e6cc-45bf-b455-28713f630d76 · outbound

This paper cites PurpleLlama/Prompt-Guard/MODEL_card.md at main · meta-llama/PurpleLlama.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset PurpleLlama/Prompt-Guard/MODEL_card.md at main · meta-llama/PurpleLlama

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.463456Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c82ad2ad-3a85-4bd6-b986-c8094a5f515a · outbound

This paper cites Introducing ChatGPT.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Introducing ChatGPT

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.448470Z

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Observation 385c8917-a90d-49f1-9959-29cbd89335d1 · outbound

This paper cites Training language models to follow instructions with human feedback.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Training language models to follow instructions with human feedback

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.433427Z

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 1295ff15-ac50-496b-97b7-3f8ecff9a8e3 · outbound

This paper cites LLM Top 10 for LLMs v1.1.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset LLM Top 10 for LLMs v1.1

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.417461Z

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 47bffec5-cf92-4c57-a84e-d1722175841f · outbound

This paper cites Comparative analysis of commercial and open source mobile device forensic tools.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Comparative analysis of commercial and open source mobile device forensic tools

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.401704Z

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 4b1075c0-17d7-438a-a368-83d6d1f6105e · outbound

This paper cites Prompt Shields in Azure AI Content Safety - Azure AI services — learn.microsoft.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Prompt Shields in Azure AI Content Safety - Azure AI services — learn.microsoft.com

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.386689Z

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 a943a691-d10b-463c-a29f-f5734ea1e677 · outbound

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

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Ignore Previous Prompt: Attack Techniques For Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.497104Z

Source-reported events for the cited work

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Observation f52f1089-11ee-45f5-b520-2eef3cc294c7 · outbound

This paper cites Protect AI.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Protect AI

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.370815Z

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 00287ae3-4c0b-431b-8540-39eb077da4f4 · outbound

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

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset GitHub - protectai/rebuff: LLM Prompt Injection Detector — github.com

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.355281Z

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 950332a8-6c69-437a-9395-6110e19750bd · outbound

This paper cites Fine-tuned deberta-v3 for prompt injection detection, 2023.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Fine-tuned deberta-v3 for prompt injection detection, 2023

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.340358Z

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 7f47e97e-9172-432c-b874-e465df99736a · outbound

This paper cites Software vulnerability detection using large language models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Software vulnerability detection using large language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.325067Z

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 e3dea4e5-0879-492f-8cdf-5e54ab5057e3 · outbound

This paper cites An Early Categorization of Prompt Injection Attacks on Large Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset An Early Categorization of Prompt Injection Attacks on Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.521815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2977a564-e3e5-463f-a9b9-4de2c52594b9 · outbound

This paper cites JasperLS/gelectra-base-injection· Hugging Face — huggingface.co.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset JasperLS/gelectra-base-injection· Hugging Face — huggingface.co

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.309856Z

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 7df8e241-3f29-4459-9962-472d1c7e3181 · outbound

This paper cites Aim | AI-FIREWALL — aim.security.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Aim | AI-FIREWALL — aim.security

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.292500Z

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 1ee374bd-7a24-4367-a9aa-40869431baad · outbound

This paper cites LLM Security — llmsecurity.net.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset LLM Security — llmsecurity.net

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.277789Z

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 a62243b7-5308-4546-aaaf-d60a47d388c9 · outbound

This paper cites Prompt Security: The Platform for GenAI Security — prompt.security.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Prompt Security: The Platform for GenAI Security — prompt.security

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.263220Z

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 26698318-605d-4aaa-aee2-c6b84aa9649a · outbound

This paper cites GitHub - utkusen/promptmap: automatically tests prompt injection attacks on ChatGPT instances — github.com.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset GitHub - utkusen/promptmap: automatically tests prompt injection attacks on ChatGPT instances — github.com

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.247942Z

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 e6e064e0-b099-4a14-bba5-7bfa7edab1ad · outbound

This paper cites Poison frogs! targeted clean-label poisoning attacks on neural networks.Advances in neural information processing systems, 31, 2018.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Poison frogs! targeted clean-label poisoning attacks on neural networks.Advances in neural information processing systems, 31, 2018

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.231146Z

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 76288a09-1f69-46a6-b74b-0c18086e71ab · outbound

This paper cites Large Language Model Alignment: A Survey.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Large Language Model Alignment: A Survey

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.556449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36f36e66-425b-4522-aff8-50e2c12a03bf · outbound

This paper cites "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.561653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebb45b06-6087-47da-a5c9-99218c4e07d5 · outbound

This paper cites Punctuation matters! stealthy backdoor attack for language models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Punctuation matters! stealthy backdoor attack for language models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.214702Z

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 63edcef3-de6a-4596-a0b1-95331d0f03a5 · outbound

This paper cites Performance comparison of intrusion detection machine learning classifiers on benchmark and new datasets.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Performance comparison of intrusion detection machine learning classifiers on benchmark and new datasets

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.199734Z

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 29647bed-1d57-40de-bbd6-a6ccfb833cd2 · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.578378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40061774-a093-4d28-8d40-3cae2840a138 · outbound

This paper cites Adversarial machine learning : a taxonomy and terminology of attacks and mitigations.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Adversarial machine learning : a taxonomy and terminology of attacks and mitigations

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.185287Z

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 bb00aece-1b72-47c1-9720-1dc9ec2bba2c · outbound

This paper cites Attention Is All You Need.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Attention Is All You Need

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.588453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a15935ae-9e59-434b-91d3-57119b68c8a7 · outbound

This paper cites Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.169638Z

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 81fc8440-7766-4a6f-856d-d0dd6335f7b2 · outbound

This paper cites Why johnny can’t encrypt: A usability evaluation of pgp 5.0.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Why johnny can’t encrypt: A usability evaluation of pgp 5.0

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.153521Z

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 caa188a9-6639-4465-9b0b-c58bc7b0797e · outbound

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

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset GitHub - whylabs/langkit: LangKit: An open-source toolkit for monitoring Large Language Models (LLMs)

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.138023Z

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 0b32148d-b854-4e5a-8db7-27862261a96a · outbound

This paper cites LLM Security Management — whylabs.ai.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset LLM Security Management — whylabs.ai

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.120929Z

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 a408c776-c4c4-44dd-85a1-ee19fb0688de · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset BloombergGPT: A Large Language Model for Finance

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.613275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53a232fe-d0e5-4c3d-8a9e-6497b47034af · outbound

This paper cites Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.618514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 886a7292-3b9c-4940-be49-106465e7bc3c · outbound

This paper cites Poisonprompt: Backdoor attack on prompt-based large language models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Poisonprompt: Backdoor attack on prompt-based large language models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.104763Z

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 117a8e02-e863-440e-ae0a-b873d7447e9b · outbound

This paper cites A survey on large language model (llm) security and privacy: The good, the bad, and the ugly.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset A survey on large language model (llm) security and privacy: The good, the bad, and the ugly

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.068403Z

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 e3a0ba8a-48b2-45d0-b84f-920f5ad57a67 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications, March.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Prompt Injection attack against LLM-integrated Applications, March

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:24:52.510330Z

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 e6b87e71-7e8c-40d9-ad61-e630e661fc20 · outbound

This paper cites GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.643207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea4ca465-4f5d-4f8d-9644-700e90a6aeb1 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Prompt Injection attack against LLM-integrated Applications

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.638460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 810e2c7d-c9bb-41f2-9cf6-5fafeb16e7c6 · outbound

This paper cites Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.654290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f7506cd-cd0a-441e-8428-5b9b0e61f98a · outbound

This paper cites InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.648153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1422b963-2216-4a98-883b-32d4ee91eb0a · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Evaluating the efficacy of LLM Safety Solutions : The Palit Benchmark Dataset Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T20:24:51.660226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers