Raphaël Khoury

Raphaël Khoury, Anderson R. Avila, Jacob Brunelle et al.

In recent years, large language models have been responsible for great advances in the field of artificial intelligence (AI). ChatGPT in particular, an AI chatbot developed and recently released by OpenAI, has taken the field to the next level. The conversational model is able not only to process human-like text, but also to translate natural language into code. However, the safety of programs generated by ChatGPT should not be overlooked. In this paper, we perform an experiment to address this issue. Specifically, we ask ChatGPT to generate a number of program and evaluate the security of the resulting source code. We further investigate whether ChatGPT can be prodded to improve the security by appropriate prompts, and discuss the ethical aspects of using AI to generate code. Results suggest that ChatGPT is aware of potential vulnerabilities, but nonetheless often generates source code that are not robust to certain attacks.

Kobra Khanmohammadi, Pooria Roy, Raphael Khoury et al.

LLM models are increasingly used to generate code, but the quality and security of this code are often uncertain. Several recent studies have raised alarm bells, indicating that such AI-generated code may be particularly vulnerable to cyberattacks. However, most of these studies rely on code that is generated specifically for the study, which raises questions about the realism of such experiments. In this study, we perform a large-scale empirical analysis of real-life code generated by ChatGPT. We evaluate code generated by ChatGPT both with respect to correctness and security and delve into the intentions of users who request code from the model. We further performed an experiment to evaluate the effectiveness of common prompt engineering strategies using real-life prompts. Our study supports earlier research that employed synthetic queries and produced proof that LLM-generated code is frequently insufficient in terms of security. Additionally, we observe that users don't ask many questions about the security characteristics of the code they ask LLMs to provide.

Nasim Soltani, Shayan Nejadshamsi, Zakaria Abou El Houda et al.

Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS towards adversarial attacks. To that end, we explore two adversarial methods for generating malicious network traffic. The first method is based on Generative Adversarial Networks (GAN) and the second one is the Fast Gradient Sign Method (FGSM). The adversarial examples generated by these methods are then used to evaluate a novel multilayer defense mechanism, specifically designed to mitigate the vulnerability of ML-based NIDS. Our solution consists of one layer of stacking classifiers and a second layer based on an autoencoder. If the incoming network data are classified as benign by the first layer, the second layer is activated to ensure that the decision made by the stacking classifier is correct. We also incorporated adversarial training to further improve the robustness of our solution. Experiments on two datasets, namely UNSW-NB15 and NSL-KDD, demonstrate that the proposed approach increases resilience to adversarial attacks.

Raphaël Khoury, Sylvain Hallé

This study carries forward the line of enquiry that seeks to characterize precisely which security policies are enforceable by runtime monitors. In this regard, Basin et al.\ recently refined the structure that helps distinguish between those actions that the monitor can potentially suppress or insert in the execution, from those that the monitor can only observe. In this paper, we generalize this model by organizing the universe of possible actions in a lattice that naturally corresponds to the levels of monitor control. We then delineate the set of properties that are enforceable under this paradigm and relate our results to previous work in the field. Finally, we explore the set of security policies that are enforceable if the monitor is given greater latitude to alter the execution of its target, which allows us to reflect on the capabilities of different types of monitors.