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Paper Citation Record · LEDGER

Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2210.09545.

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

pith.paper-citation-record.v1
2210.09545 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:24:12.241431Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:04:28.747447Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f769a2be-802e-49d2-a148-cb62a1571138 · inbound

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

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 74

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:25:21.156152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-15T06:25:20.966510Z digest=sha256:fa4e099ef3a137b7c94c731ec318b08dbdb62e7234ba9435d7c8d4ce844a0645

Observation 032f5686-5aa0-4cc7-99df-b6fbb46f5b87 · inbound

Neutralizing Backdoors through Information Conflicts for Large Language Models cites this paper.

Neutralizing Backdoors through Information Conflicts for Large Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-12T11:25:32.007402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:25:32.007402Z digest=sha256:9c3ef15c821db8b4ba93991a4cd397547970695d0d832b55886cf8643cff4695

Observation fcfdeef4-ff84-4cfc-b4d0-f927b33b2ddb · inbound

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations cites this paper.

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 207

Resolution
unresolved
no resolver link, observed 2026-08-09T00:50:00.805527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:50:00.805527Z digest=sha256:7afa62dacd2493badabe89e93127787707edf7b33a048348cbd15117218c394c

Observation 82f817e2-d9ff-4a04-a15f-f42f22c85624 · inbound

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment cites this paper.

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-16T11:24:12.241431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:24:12.241431Z digest=sha256:b8d79a082bb8eb575f24ac4e337d206f5d754bd7b5db1c3b9beb221c47b60718

Observation 8bcb31ac-ab0b-4aed-b6ab-f7b8bd0de2e3 · inbound

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures cites this paper.

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 171

Resolution
unresolved
no resolver link, observed 2026-08-16T04:28:00.951777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:28:00.951777Z digest=sha256:1ddf4df0224b57ce44fc0b7bdfef2c437f485ec00565b9ecd84dd49759c88cc2

Observation 66ceb03c-a844-4b5a-8429-cb423a7a5078 · inbound

A Systematic Review of Poisoning Attacks Against Large Language Models cites this paper.

A Systematic Review of Poisoning Attacks Against Large Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:34.563333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:34.563333Z digest=sha256:e746d86acc2423cfb4d089910648326cd30ecb78a35167f824e054eec4b34191

Observation 18ef200f-a70b-47a0-9df2-6a583814239e · inbound

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 181

Resolution
unresolved
no resolver link, observed 2026-08-06T16:24:30.487336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:24:30.487336Z digest=sha256:83e86e9bcf94396a9bf559ac577607157996f71a340f0935dd2d240595e07a7f

Observation 1dc43788-7721-4823-9d0f-612704c4b474 · inbound

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution cites this paper.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.551978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.551978Z digest=sha256:c96c2d17aca48792df43677beb5159fa6d6f151a25a2a8db92463c6aff7b3e2f

Observation 62a5fc8b-95a8-434c-a918-a514f3b50bbe · inbound

Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks cites this paper.

Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:02:08.627638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-17T22:01:09.858921Z digest=sha256:b479b2bb3bee5b46b2240ba8c99bd26a8c3c0d2729ad1f37589f11581477a2c5

Observation 680ba610-dad7-4b01-9447-52f6936411cf · inbound

Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models cites this paper.

Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T18:08:07.581777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:08:07.581777Z digest=sha256:461d4509c6f40313f39a1710c912325339fe9d5860647bbc1783091ef1447d0c

Observation 26e22186-fe39-4dba-b99b-97c81c1c6e82 · inbound

BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models cites this paper.

BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T21:02:58.931015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-14T21:01:10.756844Z digest=sha256:b9e9324d549d48345f5689c91539b0d329d01ca0eed97d7389b16c76dc401423

Observation 80a1cb2f-a269-4774-828b-dbbc367d7bb9 · inbound

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors cites this paper.

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:04:28.749090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-30T07:47:18.350953Z digest=sha256:3f41d9239629288a5adf66b6da78c0513d45a8be92d37834cfdba3dc1fc74795

Observation 31690c6c-4c21-4b05-a511-bc5304e931fd · inbound

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors cites this paper.

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-04T04:39:06.938899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:39:06.938899Z digest=sha256:5a6d462102feb6e42123267404d39abe0c86f602c472887a23b19814fa1ccea0