Detecting and Understanding Vulnerabilities in Language Models via Mechanistic Interpretability

• URL: http://arxiv.org/abs/2407.19842v1
• Date: Mon, 29 Jul 2024 09:55:34 GMT
• Title: Detecting and Understanding Vulnerabilities in Language Models via Mechanistic Interpretability
• Authors: Jorge García-Carrasco, Alejandro Maté, Juan Trujillo,
• Abstract summary: Large Language Models (LLMs) have shown impressive performance across a wide range of tasks. LLMs in particular are known to be vulnerable to adversarial attacks, where an imperceptible change to the input can mislead the output of the model. We propose a method, based on Mechanistic Interpretability (MI) techniques, to guide this process.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs), characterized by being trained on broad amounts of data in a self-supervised manner, have shown impressive performance across a wide range of tasks. Indeed, their generative abilities have aroused interest on the application of LLMs across a wide range of contexts. However, neural networks in general, and LLMs in particular, are known to be vulnerable to adversarial attacks, where an imperceptible change to the input can mislead the output of the model. This is a serious concern that impedes the use of LLMs on high-stakes applications, such as healthcare, where a wrong prediction can imply serious consequences. Even though there are many efforts on making LLMs more robust to adversarial attacks, there are almost no works that study \emph{how} and \emph{where} these vulnerabilities that make LLMs prone to adversarial attacks happen. Motivated by these facts, we explore how to localize and understand vulnerabilities, and propose a method, based on Mechanistic Interpretability (MI) techniques, to guide this process. Specifically, this method enables us to detect vulnerabilities related to a concrete task by (i) obtaining the subset of the model that is responsible for that task, (ii) generating adversarial samples for that task, and (iii) using MI techniques together with the previous samples to discover and understand the possible vulnerabilities. We showcase our method on a pretrained GPT-2 Small model carrying out the task of predicting 3-letter acronyms to demonstrate its effectiveness on locating and understanding concrete vulnerabilities of the model.

Related papers

• Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
  Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks. We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction. We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2024-11-01T04:05:59Z)
• Can LLMs be Fooled? Investigating Vulnerabilities in LLMs [4.927763944523323]
  The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP) This paper will synthesize the findings from each vulnerability section and propose new directions of research and development. By understanding the focal points of current vulnerabilities, we can better anticipate and mitigate future risks.
  arXiv  Detail & Related papers  (2024-07-30T04:08:00Z)
• A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
  arXiv  Detail & Related papers  (2024-07-10T06:57:58Z)
• Defending Large Language Models Against Attacks With Residual Stream Activation Analysis [0.0]
  Large Language Models (LLMs) are vulnerable to adversarial threats. This paper presents an innovative defensive strategy, given white box access to an LLM. We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification.
  arXiv  Detail & Related papers  (2024-06-05T13:06:33Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models [18.624280305864804]
  Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP) This paper presents a comprehensive survey of the various forms of attacks targeting LLMs. We delve into topics such as adversarial attacks that aim to manipulate model outputs, data poisoning that affects model training, and privacy concerns related to training data exploitation.
  arXiv  Detail & Related papers  (2024-03-03T04:46:21Z)
• Data Poisoning for In-context Learning [49.77204165250528]
  In-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks. This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks. We introduce ICLPoison, a specialized attacking framework conceived to exploit the learning mechanisms of ICL.
  arXiv  Detail & Related papers  (2024-02-03T14:20:20Z)
• Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks [10.732558183444985]
  Malicious actors can covertly exploit large language models (LLMs) vulnerabilities through poisoning attacks aimed at generating undesirable outputs. This paper explores various poisoning techniques to assess their effectiveness across a range of generative tasks. We show that it is possible to successfully poison an LLM during the fine-tuning stage using as little as 1% of the total tuning data samples.
  arXiv  Detail & Related papers  (2023-12-07T23:26:06Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.