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Paper Citation Record · LEDGER

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation

As of 21 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2412.13705.

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

pith.paper-citation-record.v1
2412.13705 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:57:06.247853Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:46:19.393383Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b94804a7-7e0c-492e-a51b-339adc53cdb0 · outbound

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

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Poisonprompt: Backdoor attack on prompt-based large language models

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T12:57:06.557548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T12:57:06.170765Z digest=sha256:a1fc17642de23744355c37c46532abfe9401633afe74a8a2d476a73c4c48b400

Observation f90fc722-6c8d-4348-83af-1b9cbee6f6fb · outbound

This paper cites Generating Fluent Adversarial Examples for Natural Languages.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Generating Fluent Adversarial Examples for Natural Languages

Reference 5

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verified exact
local_arxiv, observed 2026-08-11T12:57:06.476595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T12:57:06.181327Z digest=sha256:349d64b55c80e64a5ffc15a0622d6fbcd6c7db436ffe8ad42e3b4e6fa12e8d9f

Observation 859cf6ee-ec06-4632-b59d-5f46e398e79c · outbound

This paper cites Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.191673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.191673Z digest=sha256:1c1e392ae0fc2ad8725e0e16c01d56796a425913b875b2ac0c2a23312bc6653e

Observation 99f83dc0-3603-465d-a36d-76c908c9ab14 · outbound

This paper cites Efficient Adversarial Training in LLMs with Continuous Attacks.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Efficient Adversarial Training in LLMs with Continuous Attacks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.196720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.196720Z digest=sha256:579662911b5077a19a6d80b31e181f46fa8af4331206699cc71d41207adff07c

Observation b9687f5e-8ce3-42c1-9d87-e8cecd757124 · outbound

This paper cites Certifying LLM Safety against Adversarial Prompting.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Certifying LLM Safety against Adversarial Prompting

Reference 9

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unresolved
no resolver link, observed 2026-08-11T12:57:06.202073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.202073Z digest=sha256:135b1dce55249a9347e68419841fe94a1ac946e5cf63fb9819a1a9fa9d0fc157

Observation b96d59f3-4ef7-42ae-a507-3112b88d60d9 · outbound

This paper cites BAE: BERT-based Adversarial Examples for Text Classification.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation BAE: BERT-based Adversarial Examples for Text Classification

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.207171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.207171Z digest=sha256:844d4808c83b9f3eddd426473eff32493784191c8997da179eea96198d32217f

Observation 4078866e-aba9-4a0f-b8bf-2e96c51bede3 · outbound

This paper cites Prompt Stealing Attacks Against Large Language Models.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Prompt Stealing Attacks Against Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.212588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.212588Z digest=sha256:bb7b8b4bd8fc8c40f7fe4f4f128896242d80cde4f37466b5b436903aba8505a1

Observation 85e3ca18-1eb1-4d03-871d-965b5cde0181 · outbound

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

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.217313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.217313Z digest=sha256:d041fc4df50f73f291c70237a889d2d592b207724fbcfc373f88b41319f3c1c1

Observation fe4dbcd4-c924-49ef-861d-03c4582ed087 · outbound

This paper cites A closer look at adversarial suffix learning for jailbreaking LLMs.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation A closer look at adversarial suffix learning for jailbreaking LLMs

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T12:57:06.541273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T12:57:06.222474Z digest=sha256:753c06705704d864dc5387a1fd676d8962c47a0112f850ba4e4904f8b90dcf55

Observation 01064f47-da56-47e6-9fcc-d4154fdffc1b · outbound

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

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Gemma: Open Models Based on Gemini Research and Technology

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.227147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.227147Z digest=sha256:01e8dfb76300c84055f04ab7313e703f269ff1e6be55824605b3c62f99c7888a

Observation 2e4efc74-44ab-4342-8bac-d1e852c64037 · outbound

This paper cites Mistral 7B.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Mistral 7B

Reference 15

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unresolved
no resolver link, observed 2026-08-11T12:57:06.232212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.232212Z digest=sha256:b27b3809c1283cbb3b1313fc62904ff9024ec43852c98e69757b83a900a73011

Observation cdb648a9-50a4-4cd1-bc55-d6c518615a3f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.237125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.237125Z digest=sha256:72bdff625b8877bea7e8bc46d428887dfd554bfbdc768518162904f18b07e031

Observation 69e3529a-6040-474a-821d-0dd0412edb6a · outbound

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

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.242039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.242039Z digest=sha256:0793704a5a256ec5d48f4021534e4e454478390619a65f35d6cb02243c20d7ee

Observation a8fd48f0-1b5d-4283-b2e9-8b3c6f13f295 · outbound

This paper cites All Languages Matter: On the Multilingual Safety of Large Language Models.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation All Languages Matter: On the Multilingual Safety of Large Language Models

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.186601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.186601Z digest=sha256:5222214b6e926a9448c7f1534b812809872f124cbf12cee5e5b642126d670a65

Observation 7b6edafa-6db6-4535-92a5-cc31c4735d63 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation BERTScore: Evaluating Text Generation with BERT

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.247853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.247853Z digest=sha256:5e78a2fb79711c418498fd6a712e0fdfab026c97e546e23fdcb59fa6d7ae0550

Observation 793f5185-0344-40be-a117-8a0723f518d9 · outbound

This paper cites Generative AI Text Classification using Ensemble LLM Approaches.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation Generative AI Text Classification using Ensemble LLM Approaches

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T12:57:06.159703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.159703Z digest=sha256:4fd142e3d7abef9596ca47cd3ed230804cfd22df96891313b634804d9d109029

Observation 2db8fa1d-12b9-43c0-b146-e254233ba213 · outbound

This paper cites M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning

Reference 2023

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unresolved
no resolver link, observed 2026-08-11T12:57:06.165383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.165383Z digest=sha256:34a28971c29482781d51442d2489cd374673f998893b5e6e55554e6fbb6bd5fb

Observation b5a25a57-915c-457e-8355-faca9fa07997 · outbound

This paper cites An LLM can Fool Itself: A Prompt-Based Adversarial Attack.

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 2024

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unresolved
no resolver link, observed 2026-08-11T12:57:06.175752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:57:06.175752Z digest=sha256:8a260af13f879dc35757b4197d804d7eb7dd06afb0d974abc03c0c8e7f302880

Pith citing papers

Observation a3023e00-af76-4fec-92e1-8133fdb83385 · inbound

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation

Reference 156

Resolution
unresolved
no resolver link, observed 2026-08-04T17:46:19.393383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:46:19.393383Z digest=sha256:a5f7898a27b15f41678ba3bcfe4de3856f0d9c64e9db14941bbf4eb21eb234eb