A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries

• URL: http://arxiv.org/abs/2310.08153v1
• Date: Thu, 12 Oct 2023 09:18:26 GMT
• Title: A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries
• Authors: Antoine Geimer, Mathéo Vergnolle, Frédéric Recoules, Lesly-Ann Daniel, Sébastien Bardin, Clémentine Maurice,
• Abstract summary: We surveyed the literature to build a classification of 34 side-channel detection frameworks. We then built a benchmark of representative cryptographic operations on a selection of 5 promising detection tools. We offer a classification of recently published side-channel vulnerabilities. We find that existing tools can struggle to find vulnerabilities for a variety of reasons, mainly the lack of support for SIMD instructions, implicit flows, and internal secret generation.
• Score: 6.826526973994114
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To protect cryptographic implementations from side-channel vulnerabilities, developers must adopt constant-time programming practices. As these can be error-prone, many side-channel detection tools have been proposed. Despite this, such vulnerabilities are still manually found in cryptographic libraries. While a recent paper by Jancar et al. shows that developers rarely perform side-channel detection, it is unclear if existing detection tools could have found these vulnerabilities in the first place. To answer this question, we surveyed the literature to build a classification of 34 side-channel detection frameworks. The classification we offer compares multiple criteria, including the methods used, the scalability of the analysis or the threat model considered. We then built a unified common benchmark of representative cryptographic operations on a selection of 5 promising detection tools. This benchmark allows us to better compare the capabilities of each tool, and the scalability of their analysis. Additionally, we offer a classification of recently published side-channel vulnerabilities. We then test each of the selected tools on benchmarks reproducing a subset of these vulnerabilities as well as the context in which they appear. We find that existing tools can struggle to find vulnerabilities for a variety of reasons, mainly the lack of support for SIMD instructions, implicit flows, and internal secret generation. Based on our findings, we develop a set of recommendations for the research community and cryptographic library developers, with the goal to improve the effectiveness of side-channel detection tools.

Related papers

• Impact of Code Transformation on Detection of Smart Contract Vulnerabilities [0.0]
  This paper presents a method for improving the quantity and quality of smart contract vulnerability datasets. The approach centers around semantic-preserving code transformation, a technique that modifies the source code structure without altering its semantic meaning. The improved results show that many newly created vulnerabilities can bypass tools and the false reporting rate goes up to 100%.
  arXiv  Detail & Related papers  (2024-10-29T03:08:25Z)
• Divide and Conquer based Symbolic Vulnerability Detection [0.16385815610837165]
  This paper presents a vulnerability detection approach based on symbolic execution and control flow graph analysis. Our approach employs a divide-and-conquer algorithm to eliminate irrelevant program information.
  arXiv  Detail & Related papers  (2024-09-20T13:09:07Z)
• The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
  Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies. We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
  arXiv  Detail & Related papers  (2024-09-10T10:12:37Z)
• Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We? [14.974832502863526]
  In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static application security testing (SAST) tools have been proposed for detecting vulnerabilities in smart contracts. In this paper, we propose an up-to-date and fine-grained taxonomy that includes 45 unique vulnerability types for smart contracts.
  arXiv  Detail & Related papers  (2024-04-28T13:40:18Z)
• Exploiting Library Vulnerability via Migration Based Automating Test Generation [16.39796265296833]
  In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. Vulnerability exploits, as code snippets provided for reproducing vulnerabilities after disclosure, contain a wealth of vulnerability-related information. This study proposes a new method based on vulnerability exploits, called VESTA, which provides vulnerability exploit tests as the basis for developers to decide whether to update dependencies.
  arXiv  Detail & Related papers  (2023-12-15T06:46:45Z)
• Identifying Vulnerabilities in Smart Contracts using Interval Analysis [0.0]
  This paper focuses on utilizing interval analysis, an existing static analysis method, for detecting vulnerabilities in smart contracts. We present a selection of motivating examples featuring vulnerable smart contracts and share the results from our experiments conducted with various existing detection tools.
  arXiv  Detail & Related papers  (2023-09-25T01:17:56Z)
• DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection [55.70982767084996]
  A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions. DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
  arXiv  Detail & Related papers  (2023-07-04T01:34:41Z)
• Verifying the Robustness of Automatic Credibility Assessment [79.08422736721764]
  Text classification methods have been widely investigated as a way to detect content of low credibility. In some cases insignificant changes in input text can mislead the models. We introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity [89.26308254637702]
  We propose a detection strategy to highlight adversarial support sets. We make use of feature preserving autoencoder filtering and also the concept of self-similarity of a support set to perform this detection. Our method is attack-agnostic and also the first to explore detection for few-shot classifiers to the best of our knowledge.
  arXiv  Detail & Related papers  (2020-12-09T14:13:41Z)

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.