Paper Citation Record · LEDGER

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks

As of 5 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2606.28962.

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

pith.paper-citation-record.v1
2606.28962 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T09:39:35.673341Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

31 of 31 outbound references displayed

verified exact11
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1281a2dc-2f0b-4681-b28c-3506010052db · outbound

This paper cites Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.979810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 24bc865a-0128-415d-87f9-e41886342922 · outbound

This paper cites LLM-FP4: 4-Bit Floating-Point Quantized Transformers.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:44:37.644081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 900c2faf-7a30-4f30-a29b-7227edf003bf · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Qlora: Efficient finetuning of quantized llms,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.976220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7abdfcc3-95c9-41c4-8a7c-733d4651512a · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Pytorch: An imperative style, high-performance deep learning library,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.977990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bc96b441-376c-4e3d-b046-3efdfac02eb2 · outbound

This paper cites Nearest is not dearest: Towards practical defense against quantization-conditioned backdoor attacks,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Nearest is not dearest: Towards practical defense against quantization-conditioned backdoor attacks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:27.009490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2e802bac-419e-4f0a-89b9-e825a6f76236 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.646550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4743c9a7-4ce2-4148-9899-4d37719827c7 · outbound

This paper cites A comprehensive study on quantization techniques for large language models,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks A comprehensive study on quantization techniques for large language models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:27.003625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2c98d7cc-db9c-463d-b628-0ec49e7c83c5 · outbound

This paper cites Contemporary advances in neural network quantization: A survey,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Contemporary advances in neural network quantization: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:27.007381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3f347a48-8286-4266-9bbc-2fdf7ba2bf55 · outbound

This paper cites Optimizing llms using quantization for mobile execution,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Optimizing llms using quantization for mobile execution,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:27.000241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 84d7e334-150b-4781-bdd4-9078d78533ae · outbound

This paper cites Chain-of-scrutiny: Detecting backdoor attacks for large language models.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Chain-of-scrutiny: Detecting backdoor attacks for large language models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:44:37.649202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 95b1d41d-eab0-4f37-9f14-78d4e345e1f1 · outbound

This paper cites Ex- ploring clean label backdoor attacks and defense in language models,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Ex- ploring clean label backdoor attacks and defense in language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.996551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0685c087-0354-46ee-85c4-07b18f4a1766 · outbound

This paper cites Tamper-Resistant Safeguards for Open-Weight LLMs.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Tamper-Resistant Safeguards for Open-Weight LLMs

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:44:37.661358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8823bb95-e51d-437c-96f9-54524f67b6d2 · outbound

This paper cites Vaccine: Perturbation-aware alignment for large language model,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Vaccine: Perturbation-aware alignment for large language model,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.998444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bc5b1ea4-f2d3-4ae5-a833-f5211039a22d · outbound

This paper cites Exploiting llm quantization,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Exploiting llm quantization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.993024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 11983d19-e6cc-4ce5-9b23-e48c41e8490b · outbound

This paper cites Understanding the threats of trojaned quantized neural network in model supply chains,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Understanding the threats of trojaned quantized neural network in model supply chains,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.994740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f5b9f97c-8cf8-4493-a3d5-7a53768de5c4 · outbound

This paper cites Qu-anti-zation: Exploiting quantization artifacts for achieving adversarial outcomes,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Qu-anti-zation: Exploiting quantization artifacts for achieving adversarial outcomes,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.983895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2a98176e-5d39-4c93-9665-4a7b94f4fb3d · outbound

This paper cites Stealthy backdoors as compression artifacts,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Stealthy backdoors as compression artifacts,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.985850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 94228f8f-de66-4c7d-8fc1-f5ef6a1c59b2 · outbound

This paper cites Quantization backdoors to deep learning commercial frameworks,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Quantization backdoors to deep learning commercial frameworks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.987613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 05eeea3e-bcb4-4afb-8071-d76fad200cc0 · outbound

This paper cites Hugging face–the ai community building the future,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Hugging face–the ai community building the future,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.989302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 29428506-565b-4271-abbb-0efc400d2690 · outbound

This paper cites Qwen2.5-Coder Technical Report.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Qwen2.5-Coder Technical Report

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.665962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c161d47b-6d5a-4fc0-9a85-5470138308c2 · outbound

This paper cites StarCoder: may the source be with you!.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks StarCoder: may the source be with you!

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.636146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 061d821a-a4b8-4fd4-a0ef-37d86c0137a9 · outbound

This paper cites Phi-2: The surprising power of small language models,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Phi-2: The surprising power of small language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.986628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3afd703d-45f9-4454-9029-b0c4174d0d69 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Gemma: Open Models Based on Gemini Research and Technology

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.639134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 62899024-f307-4e23-bf02-21dadc0c3de1 · outbound

This paper cites Large language models for code: Security hardening and adversarial testing,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Large language models for code: Security hardening and adversarial testing,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:26.993741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6e155315-3940-4eee-948b-48399112e9d2 · outbound

This paper cites On the exploitability of instruction tuning,.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks On the exploitability of instruction tuning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:27.001896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9564006c-ca0c-4c86-9442-3ebff066ffcf · outbound

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

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Training language models to follow instructions with human feedback,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T18:26:27.005369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f79b1601-799f-4c58-94dd-ca14b448f5c5 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.641457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 75cab7d1-8689-4cb2-ab6c-0e7f8c70a090 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Measuring Massive Multitask Language Understanding

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.663662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9d431991-c891-4e12-94d6-9b0af69e0bb0 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Evaluating Large Language Models Trained on Code

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.658814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5604e195-9b28-4d59-8bb6-478920755c4c · outbound

This paper cites Program Synthesis with Large Language Models.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks Program Synthesis with Large Language Models

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:44:37.656177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e6ad26a2-4a95-4594-a9d2-6436636e68cd · outbound

This paper cites DeepSeek-V3 Technical Report.

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks DeepSeek-V3 Technical Report

Reference 31

Resolution: malformed identifier; local_arxiv, observed 2026-06-30T09:44:37.651410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.