Summary
This paper addresses the question of whether is possible to manipulate open-sourced LLMs to achieve the generation of undesired content. The authors propose a method called Probability Manipulation (ProMan) that intervenes in the generation process of LLMs to guide them in generating harmful or sensitive content. The key idea behind ProMan is to directly manipulate the probability distribution of tokens in the generation process of the LMs to guide them in generating specific tokens, in order to create an affirmative prefix. Secondly, they alternate specific tokens during the generation with their predefined negative pairs (e.g. unethical → ethical). They conduct experiments on exposing harmful content and on private leakage with four open-sourced LLMs and demonstrate various case studies of undesired generations. The paper also discusses potential countermeasures to mitigate harmful generations. The contributions of this work include the introduction of ProMan, the empirical evaluation of its performance, and the exploration of potential defence strategies for open-sourced LLMs.
Strengths
1) The concept of manipulating the probability distribution in order to produce affirmative prefixes and negation reversing, while unesecery in the specific case, is indeed interesting. Negation reversing and token alternations through manipulating the probability distribution, in general, should be further explored.
2) The quality of the paper is generally good, with a well-structured presentation of the problem, methodology, and experimental results. The experiments conducted on different LLMs and attack scenarios provide initial evidence for the effectiveness of ProMan in generating undesired content and privacy leakage.
3) By demonstrating the ability to manipulate LLMs and generate harmful content, the paper raises important questions about the security and responsible use of these models. It contributes to the ongoing discussions on LLM safety and calls for more advanced mitigation strategies.
Weaknesses
1) The authors promise to misguide open LLMs "without careful prompt designs". They do exactly that, in a careful and complicated manner they design the prompt of the model and alternate specific tokens of the produced output (that is used as input to produce the following tokens...).
2) The interesting probability manipulation idea, in practice consists of enforcing an affirmative prefix and negating six predefined tokens.
3) The enforced production of the affirmative prefix, is identical to appending a suffix to the prompt, making it unnecessarily complicated and less novel than what is presented.
4) The negation reversing aspect, while the only novelty of the paper, is limited in scope as it only changes six tokens with their predefined negative pairs.
5) The paper only compares ProMan with two baseline attacks, one heuristic and one optimization-based, but fails to provide a comprehensive evaluation against a wider range of existing attack methods.
6) It would be beneficial to expand the evaluation to larger LLMs to provide more robust and reliable results on scale.
7) For the privacy leakage experiment the evaluation is based on a relatively small evaluation set of 100 queries.
8) In the Possible Countermeasures section the paper presents almost generic methods from the literature to address current issues with LLMs. It would be beneficial to discuss specific solutions for the specific proposed method or for "prompt hacking" in general.
9) The paper lacks a thorough analysis and discussion of the potential ethical implications of the proposed method. Given that the goal of the work is to expose the limitations of alignment in preventing misuse of LLMs, it would be important to address the potential harm and ethical concerns associated with such misuse. Providing a more comprehensive analysis of the ethical considerations and discussing potential mitigation strategies would strengthen the paper's contribution to addressing the responsible development and usage of LLMs.
Questions
1. How scalable is ProMan? Have the authors considered evaluating the method on larger LLMs to determine its effectiveness and robustness on a larger scale?
2. In the Possible Countermeasures section, the authors present generic methods from the literature. Can they discuss specific solutions or potential countermeasures that are more tailored to address the proposed method of ProMan or the broader concept of "prompt hacking"?
3. Given that appending an affirmative prefix to the prompt achieves a similar outcome as ProMan, can the authors explain the specific advantages or novelty of ProMan in generating the affirmative prefix? How does ProMan go beyond what can be achieved through simpler methods?
4. Can the authors provide insights into the potential impact of ProMan on the alignment of LLMs and the implications for the development and usage of open-source LLMs? How can the findings of this work contribute to improving the alignment and safety measures of LLMs in the future?
5. Are there any limitations or challenges that the authors encountered during the implementation or evaluation of ProMan that may impact its practicality or effectiveness?
Rating
3: reject, not good enough
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Ethics concerns
One concern regarding the ethical considerations in this paper is the potential harm that can arise from the misuse of LLMs. The paper demonstrates how ProMan can be used to generate harmful, biased, or privacy-sensitive content. While the authors have taken precautions to obscure potentially biased content in the examples, there is still the risk that these techniques could be used to generate and spread harmful or misleading information.
Additionally, the paper does not provide an in-depth analysis or discussion of the ethical implications associated with the proposed method.