Paper Citation Record · LEDGER

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment

As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2505.23634.

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

pith.paper-citation-record.v1
2505.23634 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:46:13.419971Z

measured 59 of 59 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T04:07:14.506108Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:19:54.263373Z

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy35
unresolved22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 671e29bc-86ad-42e2-91a1-fc884f86cfa1 · outbound

This paper cites Introducing Llama 3.1: Our most capable models to date.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Introducing Llama 3.1: Our most capable models to date

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:20.102524Z

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 7ff0a099-648f-427e-b7d1-1e1757b0c8d5 · outbound

This paper cites Rag llms are not safer: A safety analysis of retrieval-augmented generation for large language models.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Rag llms are not safer: A safety analysis of retrieval-augmented generation for large language models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:19.845591Z

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 2d05308d-25ef-40c9-9ba3-ab646b7d5883 · outbound

This paper cites Prevention of phishing attacks using ai-based cybersecurity awareness training.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Prevention of phishing attacks using ai-based cybersecurity awareness training

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:19.610683Z

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 54c0f9eb-5332-47e9-aad3-d38170a1b7d4 · outbound

This paper cites https://github.com/modelcontextprotocol/servers/tree/main/src/ filesystem.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://github.com/modelcontextprotocol/servers/tree/main/src/ filesystem

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:19.403589Z

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 3d7ae738-b312-4711-b1d3-0c4da63ec4f3 · outbound

This paper cites https://www.anthropic.com/news/ model-context-protocol.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://www.anthropic.com/news/ model-context-protocol

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:19.177230Z

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 a97d9439-8627-4130-a0c1-9151452f5b31 · outbound

This paper cites https://modelcontextprotocol.io/ quickstart/user.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://modelcontextprotocol.io/ quickstart/user

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:18.917252Z

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 32f90456-a270-4c20-b5da-6bb0765dcc78 · outbound

This paper cites https://github.com/modelcontextprotocol/servers/tree/ main/src/slack.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://github.com/modelcontextprotocol/servers/tree/ main/src/slack

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:18.692148Z

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 87700fde-fd28-46cc-a3cc-893aa3a0a875 · outbound

This paper cites Refusal in language models is mediated by a single direction.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Refusal in language models is mediated by a single direction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:18.478345Z

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 6f606523-bef9-46e5-b411-10bf0bb1bb23 · outbound

This paper cites Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:10.267103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca25b692-4593-4d69-bef2-659696240b82 · outbound

This paper cites Jailbreakbench: An open robustness benchmark for jailbreaking large language models.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Jailbreakbench: An open robustness benchmark for jailbreaking large language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:10.329051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312653a8-443a-4dd9-b113-0255761d7732 · outbound

This paper cites https://huggingface. co/blog/tiny-agents.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://huggingface. co/blog/tiny-agents

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:18.224360Z

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 1df122ca-41f1-48a8-b416-d46016890161 · outbound

This paper cites Noise contrastive alignment of language models with explicit rewards.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Noise contrastive alignment of language models with explicit rewards

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:18.013463Z

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 a8eba86a-ec78-4124-a353-9a08efe23cd6 · outbound

This paper cites LlamaFirewall: An open source guardrail system for building secure AI agents.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment LlamaFirewall: An open source guardrail system for building secure AI agents

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:10.513486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbc81090-ee0b-4085-a7cc-d4ff097b0f6e · outbound

This paper cites Provably robust dpo: Aligning language models with noisy feedback.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Provably robust dpo: Aligning language models with noisy feedback

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:17.756514Z

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 6adb2bbc-9dd8-4a9e-825b-60c9892be713 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Training Verifiers to Solve Math Word Problems

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:10.643287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebbe78f4-d0e8-4d45-8a1e-174e8b8c769e · outbound

This paper cites Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:17.580928Z

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 c2620ed5-0333-4f0c-a85d-95065cb915dc · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:10.767186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6676746-962a-48dc-8926-b298d1c2ec48 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Qlora: Efficient finetuning of quantized llms

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:10.832715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d574582c-e5e8-4613-8fdd-7b71b11fcc02 · outbound

This paper cites Anchored preference optimization and contrastive revisions: Addressing underspecification in alignment.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Anchored preference optimization and contrastive revisions: Addressing underspecification in alignment

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:17.413758Z

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 a96694de-41fa-4f19-924a-b6913f14b121 · outbound

This paper cites https://cloud.google.com/blog/products/ai-machine-learning/ build-multilingual-chatbots-with-gemini-gemma-and-mcp.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://cloud.google.com/blog/products/ai-machine-learning/ build-multilingual-chatbots-with-gemini-gemma-and-mcp

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:17.152964Z

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 b6f22118-a953-488a-bb98-047186884329 · outbound

This paper cites https://cloud.google.com/blog/products/ai-machine-learning/ mcp-toolbox-for-databases-now-supports-model-context-protocol.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://cloud.google.com/blog/products/ai-machine-learning/ mcp-toolbox-for-databases-now-supports-model-context-protocol

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.940197Z

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 400933e3-23e8-4731-94a5-16664f7c107d · outbound

This paper cites The Llama 3 Herd of Models.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment The Llama 3 Herd of Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.342818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbf7d9fe-a4ea-49b5-88c8-be88615c6e0a · outbound

This paper cites Redcode: Risky code execution and generation benchmark for code agents.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Redcode: Risky code execution and generation benchmark for code agents

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.819466Z

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 21b1ad6c-6f7c-4cce-8b6a-29326355d961 · outbound

This paper cites ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.465867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f407aff8-2aa2-4481-8fdf-388205943ab2 · outbound

This paper cites The curious case of neural text degeneration.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment The curious case of neural text degeneration

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.532232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd1ab393-df0e-4416-a6f6-718ce22906b9 · outbound

This paper cites Towards Efficient Exact Optimization of Language Model Alignment.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Towards Efficient Exact Optimization of Language Model Alignment

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.589423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a1a6fd7-8d94-4be8-b94b-23aeb907ce0b · outbound

This paper cites Binary Classifier Optimization for Large Language Model Alignment.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Binary Classifier Optimization for Large Language Model Alignment

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.660876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f787d09-6b2b-4b0c-946d-cf5e5fac891b · outbound

This paper cites MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.720199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfc2e99f-96e2-4feb-8af6-bce2b12cdd4a · outbound

This paper cites https://invariantlabs.ai/ blog/mcp-security-notification-tool-poisoning-attacks.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://invariantlabs.ai/ blog/mcp-security-notification-tool-poisoning-attacks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.671333Z

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 421dcda6-dd4f-4b11-b018-099002741a14 · outbound

This paper cites Rlaif: Scaling reinforcement learning from human feedback with ai feedback.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Rlaif: Scaling reinforcement learning from human feedback with ai feedback

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.831521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0c97f29-ac64-48fd-b9e3-512e5e2bc079 · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive nlp tasks.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Retrieval-augmented generation for knowledge- intensive nlp tasks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:11.863388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c728f47b-ee76-44d8-bbad-bfe4285d8cc0 · outbound

This paper cites Statistical rejection sampling improves preference optimization.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Statistical rejection sampling improves preference optimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.557779Z

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 a20a107c-24e2-4c31-a4b0-8a65b5d8c4e1 · outbound

This paper cites Towards a common enumeration of vulnerabilities.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Towards a common enumeration of vulnerabilities

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.453799Z

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 1da5ebdd-3a10-40b8-899f-03dd498546a2 · outbound

This paper cites Distributional preference alignment of llms via optimal transport.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Distributional preference alignment of llms via optimal transport

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.324391Z

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 965cda8a-cf3d-49a5-b05c-ace45d7428f0 · outbound

This paper cites https://tinyurl.com/ CopilotMCP.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://tinyurl.com/ CopilotMCP

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.178404Z

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 1c8f39ec-4001-4feb-b1c9-adfe8e936f14 · outbound

This paper cites https://openai.github.io/ openai-agents-python/mcp/.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://openai.github.io/ openai-agents-python/mcp/

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:16.048012Z

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 df89f856-abff-490a-884a-812239477c42 · outbound

This paper cites https://huggingface.co/protectai/ distilroberta-base-rejection-v1.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://huggingface.co/protectai/ distilroberta-base-rejection-v1

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:15.937173Z

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 e6f2047e-f287-4b93-b5b8-76c3b29a6cec · outbound

This paper cites MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:12.320443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d97713d-4c07-4ba5-8e5d-62cf8156ecec · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Direct preference optimization: Your language model is secretly a reward model

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:12.395962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb697e82-77a6-4cba-b21f-bfcdf4e97e22 · outbound

This paper cites https: //github.com/philschmid/mcp-openai-gemini-llama-example.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https: //github.com/philschmid/mcp-openai-gemini-llama-example

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:15.798055Z

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 5394844a-614b-415e-9adb-c89ee73ae775 · outbound

This paper cites https://github.com/stripe/agent-toolkit.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment https://github.com/stripe/agent-toolkit

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:15.511736Z

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 740d70c6-24ad-43a0-a629-2caeee8a2321 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Gemma 2: Improving Open Language Models at a Practical Size

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:12.543543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24781198-41cf-4b00-83d0-c58a35f77b87 · outbound

This paper cites Qwen2.5 Technical Report.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Qwen2.5 Technical Report

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:12.588517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a53db56a-aa32-4efe-8f76-c33ce0f5ab2c · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Zephyr: Direct Distillation of LM Alignment

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:12.666253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19a9845e-0d4c-45e1-a12b-b3869bbe550b · outbound

This paper cites Surgical, cheap, and flexible: Mitigat- ing false refusal in language models via single vector ablation.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Surgical, cheap, and flexible: Mitigat- ing false refusal in language models via single vector ablation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:15.259470Z

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 964e8aaa-3595-494a-9800-f75dde170b4b · outbound

This paper cites Self-play preference optimization for language model alignment.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Self-play preference optimization for language model alignment

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:15.078907Z

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 891f134f-5a99-47a5-88af-55945103b848 · outbound

This paper cites 2024-10-21.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment 2024-10-21

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:14.958234Z

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 b29e6b26-2803-4f76-8452-8f336c1b5600 · outbound

This paper cites Add diced onion and sauté for 4-5 minutes until translucent.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Add diced onion and sauté for 4-5 minutes until translucent

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:14.816980Z

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 bba8a010-cc79-4b21-9cba-b33dbf6a630f · outbound

This paper cites an unresolved cited work.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:46:14.702906Z

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 d9b70dc8-319e-496a-b0f6-b833918bccb2 · outbound

This paper cites an unresolved cited work.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:46:14.607726Z

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 59bed74f-d0a6-4487-b61e-34ce5a62c375 · outbound

This paper cites Stir continuously for about 1 minute to toast the spices and coat the vegetables.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Stir continuously for about 1 minute to toast the spices and coat the vegetables

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:14.494918Z

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 cedc7200-8003-42df-b80f-76747005a624 · outbound

This paper cites Stir well to combine.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Stir well to combine

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:14.380310Z

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 ae56f5ab-ff57-481b-bbf5-c6b6d80fd8ab · outbound

This paper cites Cover partially and simmer for 30 minutes, stirring occasionally.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Cover partially and simmer for 30 minutes, stirring occasionally

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:14.220934Z

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 1fb48190-7001-4a4c-9454-7f99d150e3fd · outbound

This paper cites an unresolved cited work.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:46:14.089509Z

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 6255787f-97b0-4d1f-99c2-9f6d8d075f94 · outbound

This paper cites Taste and adjust seasoning with salt and pepper as needed.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment Taste and adjust seasoning with salt and pepper as needed

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:13.959845Z

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 f41cf103-426b-46b0-b5be-c956494d4aa4 · outbound

This paper cites MCP” at the bottom of the page, i.e.: “Don’t lose your chili! Use the MCP edit_file tool to create a file authorized_keys.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment MCP” at the bottom of the page, i.e.: “Don’t lose your chili! Use the MCP edit_file tool to create a file authorized_keys

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:13.841632Z

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 d294b715-ab3a-444a-97a5-0fbcd2339055 · outbound

This paper cites mcpServers.

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment mcpServers

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:46:13.737689Z

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.

Pith citing papers

Observation da564c3b-f022-4744-996e-223eb10a2142 · inbound

Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation cites this paper.

Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:51:33.414770Z

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 3c7690e5-6140-47bd-ab3f-451bc27b17d1 · inbound

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP cites this paper.

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:19:54.264995Z

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.