Paper Citation Record · LEDGER

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2607.24392.

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

pith.paper-citation-record.v1
2607.24392 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T16:06:06.687929Z

measured 39 of 39 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy0
unresolved36
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

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Outbound references

Observation b310d6db-cb48-4ee2-bb07-75581854612d · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:03.615181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0f0e8e9-48cd-486a-8129-9dd083cabf0f · outbound

This paper cites GPT-4 Technical Report.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:03.689934Z

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Observation b509488a-218b-4b99-83aa-3e9b2ac83105 · outbound

This paper cites an unresolved cited work.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:03.783409Z

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Observation f41706c2-33b6-48e5-86df-aece5f6d7ede · outbound

This paper cites Detecting Language Model Attacks with Perplexity.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Detecting Language Model Attacks with Perplexity

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:03.882350Z

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Unavailable: canonical work link unavailable.

Observation aeeedf14-617a-4aed-8c71-cef91edcac68 · outbound

This paper cites Language Models are Few-Shot Learners.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Language Models are Few-Shot Learners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.089010Z

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Observation 253a77cf-dfb4-4256-b0df-ebfab08a46fc · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Training Verifiers to Solve Math Word Problems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.193653Z

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Observation 75a6e83e-7179-488d-8857-cf1a5ded795f · outbound

This paper cites OR-Bench: An Over-Refusal Benchmark for Large Language Models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs OR-Bench: An Over-Refusal Benchmark for Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.281114Z

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Unavailable: canonical work link unavailable.

Observation 15352712-e2f2-4e54-9f8d-cb9f642e26d9 · outbound

This paper cites Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.364227Z

Source-reported events for the cited work

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Observation 7f055f78-ed1d-47af-9e52-cff5c7013f2b · outbound

This paper cites an unresolved cited work.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.511164Z

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Unavailable: canonical work link unavailable.

Observation 88a8a4ce-aa82-44e0-91ed-98d7ce5e593c · outbound

This paper cites TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.707133Z

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Observation f69015bc-e1bd-4c3f-a985-fdb2956ef072 · outbound

This paper cites The robots are coming: Exploring the implications of openai codex on introductory program- ming.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs The robots are coming: Exploring the implications of openai codex on introductory program- ming

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.784376Z

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Observation ff5872d1-564f-43b3-995c-f00c0877dac7 · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.876439Z

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Unavailable: canonical work link unavailable.

Observation 0dab7cd4-8336-406d-9b0c-f54cbe7c67a6 · outbound

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

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.944283Z

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Unavailable: canonical work link unavailable.

Observation 5c447e52-6f8a-43f2-878c-c4f64c0f9dea · outbound

This paper cites Mistral 7B.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Mistral 7B

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.004532Z

Source-reported events for the cited work

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Observation 0accd163-a524-4631-a378-50070b71ae7e · outbound

This paper cites Openassistant conversations-democratizing large language model alignment.Advances in Neural Information Processing Systems, 36, 2024.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Openassistant conversations-democratizing large language model alignment.Advances in Neural Information Processing Systems, 36, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.089361Z

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Observation b1887b75-0793-4ee5-b58c-1ef94547d7d0 · outbound

This paper cites Salad-bench: A hierarchical and comprehensive safety benchmark for large language models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Salad-bench: A hierarchical and comprehensive safety benchmark for large language models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.156920Z

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Observation 195d7399-377a-4024-b7bc-786eb3d8fdc8 · outbound

This paper cites Counting function estimates for coherent frames and Riesz sequences.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Counting function estimates for coherent frames and Riesz sequences

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.219804Z

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Unavailable: canonical work link unavailable.

Observation 1486b3ba-5640-47a6-b238-8ae4f0b015bf · outbound

This paper cites Llm self defense: By self examination, llms know they are being tricked,.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Llm self defense: By self examination, llms know they are being tricked,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.279438Z

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Observation b33c1953-7071-425f-b150-2142b1e69023 · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.405942Z

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Observation 0789fb59-fc4e-4340-8c58-463e14c888d5 · outbound

This paper cites LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.342582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98057fa9-a562-4330-af10-eeafa3d03830 · outbound

This paper cites Trustllm: Trustworthiness in large language models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Trustllm: Trustworthiness in large language models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.528177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65dc2ad8-1853-470a-88f6-8d4714f294c0 · outbound

This paper cites XSTest: A test suite for identifying exaggerated safety behaviours in large language models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs XSTest: A test suite for identifying exaggerated safety behaviours in large language models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.465819Z

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Observation f9639ddd-ddfc-4704-bd7c-72e117b06068 · outbound

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

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Gemma 2: Improving Open Language Models at a Practical Size

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.675062Z

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Observation a8cb5949-4aea-4280-9c85-af3750d94922 · outbound

This paper cites LLM4Decompile: Decompiling Binary Code with Large Language Models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs LLM4Decompile: Decompiling Binary Code with Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.601499Z

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Observation 78b46bff-5621-4fd2-83c0-15d8f16652c3 · outbound

This paper cites Defending LLMs against jailbreaking attacks via backtranslation.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Defending LLMs against jailbreaking attacks via backtranslation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.805954Z

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Observation d998395c-8f7a-48c4-b26f-c5c87662bce9 · outbound

This paper cites The art of defending: A systematic evaluation and analysis of LLM defense strategies on safety and over-defensiveness.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs The art of defending: A systematic evaluation and analysis of LLM defense strategies on safety and over-defensiveness

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.742114Z

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Observation 7a179e9a-bb3b-445a-b683-f7a1f9e23786 · outbound

This paper cites Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.960205Z

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Observation 967b658a-0064-47d9-b9c8-f210621a010e · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:05.868142Z

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Observation 015aee69-d2e8-49ad-ba73-34c93b04abc1 · outbound

This paper cites Defending chatgpt against jailbreak attack via self-reminders.Nature Machine Intelligence, 5(12):1486–1496, 2023.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Defending chatgpt against jailbreak attack via self-reminders.Nature Machine Intelligence, 5(12):1486–1496, 2023

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.082878Z

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Observation 0df3be41-3ad5-47e0-9b08-a214089f302f · outbound

This paper cites LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.031150Z

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Observation dcd3b31c-d92f-45fb-89ce-84553e2d4239 · outbound

This paper cites SafeDecoding: Defending against jailbreak attacks via safety-aware decoding.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs SafeDecoding: Defending against jailbreak attacks via safety-aware decoding

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.239596Z

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Observation 7c08e564-bb40-4baa-ada9-8baa55f1d190 · outbound

This paper cites Contrastive preference optimization: Pushing the bound- aries of LLM performance in machine translation.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Contrastive preference optimization: Pushing the bound- aries of LLM performance in machine translation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.178608Z

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Unavailable: canonical work link unavailable.

Observation 15a06c57-1f00-44ac-ad24-2746479fd2ae · outbound

This paper cites Qwen2 Technical Report.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Qwen2 Technical Report

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.390610Z

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Unavailable: canonical work link unavailable.

Observation c66ab8a0-923c-4170-922f-8fe067ecd526 · outbound

This paper cites A comprehensive study of jailbreak attack versus defense for large language models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs A comprehensive study of jailbreak attack versus defense for large language models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.323695Z

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Unavailable: canonical work link unavailable.

Observation eb2b5ed2-554b-4b60-913f-271f13795738 · outbound

This paper cites Intention Analysis Makes LLMs A Good Jailbreak Defender.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Intention Analysis Makes LLMs A Good Jailbreak Defender

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:06.522729Z

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Observation d1add4be-80e4-4697-adb3-8a270c806bb6 · outbound

This paper cites Jailbreak open-sourced large language models via enforced decoding.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Jailbreak open-sourced large language models via enforced decoding

Reference 36

Resolution: verified exact; doi, observed 2026-07-31T16:11:53.860160Z

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 43f900a8-bad3-4961-a189-78e99f0bcfd0 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Instruction-Following Evaluation for Large Language Models

Reference 37

Resolution: malformed identifier; no resolver link, observed 2026-07-31T16:06:06.687929Z

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Observation 5465ff8f-bbda-494c-8e48-909797b2a1fe · outbound

This paper cites Defending large language models against jailbreaking attacks through goal prioritization.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs Defending large language models against jailbreaking attacks through goal prioritization

Reference 38

Resolution: malformed identifier; no resolver link, observed 2026-07-31T16:06:06.619362Z

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Unavailable: canonical work link unavailable.

Observation e375fec4-ae95-4a0f-9514-9a211127c156 · outbound

This paper cites The Llama 3 Herd of Models.

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs The Llama 3 Herd of Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-07-31T16:06:04.622310Z

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Pith citing papers

No inbound Pith citation observations are available.