# Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models

arXiv: https://arxiv.org/abs/2401.10647 · HTML: https://mlsanity.com/paper/2401.10647/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 24 of 24)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Knowledge Neurons in Pretrained Transformers](https://mlsanity.com/paper/2104.08696/) (2021)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Erasing Concepts from Diffusion Models](https://mlsanity.com/paper/2303.07345/) (2023)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions](https://mlsanity.com/paper/2309.07875/) (2023)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions](https://mlsanity.com/paper/2305.14795/) (2023)
- [Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models](https://mlsanity.com/paper/2301.04213/) (2023)
- [Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment](https://mlsanity.com/paper/2308.09662/) (2023)
- [Evaluating Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2310.19736/) (2023)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Knowledge Editing for Large Language Models: A Survey](https://mlsanity.com/paper/2310.16218/) (2023)
- [A Comprehensive Study of Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2401.01286/) (2024)
- [Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://mlsanity.com/paper/2401.04700/) (2024)
- [Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark](https://mlsanity.com/paper/2305.17553/) (2023)
- [All Languages Matter: On the Multilingual Safety of Large Language Models](https://mlsanity.com/paper/2310.00905/) (2023)
- [Unveiling the Implicit Toxicity in Large Language Models](https://mlsanity.com/paper/2311.17391/) (2023)
- [Unveiling the Pitfalls of Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2310.02129/) (2023)
- [Evaluating the Reversal Curse in Model Editing](https://mlsanity.com/paper/2310.10322/) (2023)
- [DocTER: Evaluating Document-based Knowledge Editing](https://mlsanity.com/paper/2308.09954/) (2023)

## Built on by (most cited first, 20 of 20)

- [Detoxifying Large Language Models via Knowledge Editing](https://mlsanity.com/paper/2403.14472/) (2024)
- [Jailbreaking and Mitigation of Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2410.15236/) (2024)
- [Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations](https://mlsanity.com/paper/2406.11801/) (2024)
- [JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models](https://mlsanity.com/paper/2406.09321/) (2024)
- [How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries](https://mlsanity.com/paper/2402.15302/) (2024)
- [SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models](https://mlsanity.com/paper/2406.12274/) (2024)
- [Editing Conceptual Knowledge for Large Language Models](https://mlsanity.com/paper/2403.06259/) (2024)
- [Position: Editing Large Language Models Poses Serious Safety Risks](https://mlsanity.com/paper/2502.02958/) (2025)
- [How to Make LLMs Forget: On Reversing In-Context Knowledge Edits](https://mlsanity.com/paper/2410.12586/) (2024)
- [Revealing and Mitigating Over-Attention in Knowledge Editing](https://mlsanity.com/paper/2502.14838/) (2025)
- [Tracing and Reversing Edits in LLMs](https://mlsanity.com/paper/2505.20819/) (2025)
- [Attributional Safety Failures in Large Language Models under Code-Mixed Perturbations](https://mlsanity.com/paper/2505.14469/) (2025)
- [Can LLMs be Fooled? Investigating Vulnerabilities in LLMs](https://mlsanity.com/paper/2407.20529/) (2024)
- ["Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models](https://mlsanity.com/paper/2403.00180/) (2024)
- [Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance](https://mlsanity.com/paper/2406.11139/) (2024)
- [Composable Interventions for Language Models](https://mlsanity.com/paper/2407.06483/) (2024)
- [Phonetic Perturbations Reveal Tokenizer-Rooted Safety Gaps in LLMs](https://mlsanity.com/paper/2505.14226/) (2025)
- [Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions](https://mlsanity.com/paper/2501.01872/) (2025)
- [ProSocialAlign: Preference Conditioned Test Time Alignment in Language Models](https://mlsanity.com/paper/2512.06515/) (2025)
- [Selective Knowledge Edit Reversal via Gated Singular Vector Shrinkage](https://mlsanity.com/paper/2609.02091/) (2026)

## Abstract (arXiv)

In the rapidly advancing field of artificial intelligence, the concept of Red-Teaming or Jailbreaking large language models (LLMs) has emerged as a crucial area of study. This approach is especially significant in terms of assessing and enhancing the safety and robustness of these models. This paper investigates the intricate consequences of such modifications through model editing, uncovering a complex relationship between enhancing model accuracy and preserving its ethical integrity. Our in-depth analysis reveals a striking paradox: while injecting accurate information is crucial for model reliability, it can paradoxically destabilize the model's foundational framework, resulting in unpredictable and potentially unsafe behaviors. Additionally, we propose a benchmark dataset NicheHazardQA to investigate this unsafe behavior both within the same and cross topical domain. This aspect of our research sheds light on how the edits, impact the model's safety metrics and guardrails. Our findings show that model editing serves as a cost-effective tool for topical red-teaming by methodically applying targeted edits and evaluating the resultant model behavior.