Description
In the recent years, formals methods for security and their associated tools have been used successfully both to find novel and complex attacks on many protocols [A] and to help in their standardization process. They however face a new challenge with the increasing probability of quantum computers coming into the real-world: we need to be able to provide guarantees against quantum attackers.In this talk, we will first present a broad overview of formal methods, outlining what is the general goal of the field and how we have strived toward it. We will then focus on the post-quantum issue by presenting the corresponding concrete challenges, and thus multiple ways current computational proofs of security (proof for any Polynomial Time Turing Machine attacker) can fail against a quantum attacker. We will then present the first-order logic over which Squirrel is built, the BC logic, and show based on the first part where it fails at post-quantum soundness. In a third part, we will finally present our contribution: how we made the logic and thus the Squirrel prover post-quantum sound. We will conclude by discussing some more general challenges the field may be facing in the coming years.[A] see, e.g., https://www.bbc.co.uk/news/technology-58719891 for a recent example[B] https://squirrel-prover.github.io/
Practical infos
Next sessions
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Opening Pandora's Box: White-Box Attacks on Microsoft's PhotoDNA Perceptual Hash Function
Speaker : Diane Leblanc-Albarel - KU Leuven
PhotoDNA is a widely deployed perceptual hash function used for the detection of illicit content such as Child Sexual Abuse Material (CSAM). In this talk, I will present our paper introducing the first mathematical description of Alleged PhotoDNA, a function that reproduces the outputs of PhotoDNA. Our analysis reveals several structural weaknesses: the function is piece-wise linear and[…]-
Cryptography
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Privacy
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Towards More Secure Large Language Models
Speaker : Raouf Kerkouche - Inria Lille
Large Language Models (LLMs) have achieved considerable success and are now widely used across multiple domains, highlighting their transformative impact on both technology and society. However, this widespread adoption also exposes LLMs to numerous security threats that can alter model behavior or degrade overall performance. To mitigate these threats, most research has focused on alignment[…]-
Machine learning
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