Exploiting Inaccurate Branch History in Side-Channel Attacks

• URL: http://arxiv.org/abs/2506.07263v1
• Date: Sun, 08 Jun 2025 19:46:43 GMT
• Title: Exploiting Inaccurate Branch History in Side-Channel Attacks
• Authors: Yuhui Zhu, Alessandro Biondi,
• Abstract summary: This paper examines how resource sharing and contention affect two widely implemented but underdocumented features: Bias-Free Branch Prediction and Branch History Speculation.<n>We show that these features can inadvertently modify the Branch History Buffer (BHB) update behavior and create new primitives that trigger malicious mis-speculations.<n>We present three novel attack primitives: two Spectre attacks, namely Spectre-BSE and Spectre-BHS, and a cross-privilege control flow side-channel attack called BiasScope.
• Score: 54.218160467764086
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern out-of-order CPUs heavily rely on speculative execution for performance optimization, with branch prediction serving as a cornerstone to minimize stalls and maximize efficiency. Whenever shared branch prediction resources lack proper isolation and sanitization methods, they may originate security vulnerabilities that expose sensitive data across different software contexts. This paper examines the fundamental components of modern Branch Prediction Units (BPUs) and investigates how resource sharing and contention affect two widely implemented but underdocumented features: Bias-Free Branch Prediction and Branch History Speculation. Our analysis demonstrates that these BPU features, while designed to enhance speculative execution efficiency through more accurate branch histories, can also introduce significant security risks. We show that these features can inadvertently modify the Branch History Buffer (BHB) update behavior and create new primitives that trigger malicious mis-speculations. This discovery exposes previously unknown cross-privilege attack surfaces for Branch History Injection (BHI). Based on these findings, we present three novel attack primitives: two Spectre attacks, namely Spectre-BSE and Spectre-BHS, and a cross-privilege control flow side-channel attack called BiasScope. Our research identifies corresponding patterns of vulnerable control flows and demonstrates exploitation on multiple processors. Finally, Chimera is presented: an attack demonstrator based on eBPF for a variant of Spectre-BHS that is capable of leaking kernel memory contents at 24,628 bit/s.

Related papers

• MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models [56.09354775405601]
  Model extraction attacks aim to replicate the functionality of a black-box model through query access.<n>Most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt suspicious inputs.<n>We propose MISLEADER, a novel defense strategy that does not rely on OOD assumptions.
  arXiv  Detail & Related papers  (2025-06-03T01:37:09Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Detecting speculative data flow vulnerabilities using weakest precondition reasoning [4.713817702376467]
  We introduce an approach for detecting the data flow vulnerabilities, Spectre-STL and Spectre-PSF, using weakest precondition reasoning.<n>We validate our approach on a suite of litmus tests used to validate related approaches in the literature.
  arXiv  Detail & Related papers  (2025-04-27T07:02:15Z)
• Lost and Found in Speculation: Hybrid Speculative Vulnerability Detection [15.258238125090667]
  We introduce Specure, a novel pre-silicon verification method composing hardware fuzzing with Information Flow Tracking (IFT) to address speculative execution leakages. Specure identifies previously overlooked speculative execution vulnerabilities on the RISC-V BOOM processor and explores the vulnerability search space 6.45x faster than existing fuzzing techniques.
  arXiv  Detail & Related papers  (2024-10-29T21:42:06Z)
• Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks [31.291700348439175]
  Malicious data manipulation attacks against machine learning jeopardize its reliability in safety-critical applications.<n>We propose NoiSec, a reconstruction-based intrusion detection system.<n>NoiSec disentangles the noise from the test input, extracts the underlying features from the noise, and leverages them to recognize systematic malicious manipulation.
  arXiv  Detail & Related papers  (2024-06-18T21:44:51Z)
• Watch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models [65.30406788716104]
  This work investigates the vulnerabilities of security-enhancing diffusion models. We demonstrate that these models are highly susceptible to DIFF2, a simple yet effective backdoor attack. Case studies show that DIFF2 can significantly reduce both post-purification and certified accuracy across benchmark datasets and models.
  arXiv  Detail & Related papers  (2024-06-14T02:39:43Z)
• Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM [12.164927192334748]
  We propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information. We show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber.
  arXiv  Detail & Related papers  (2024-01-25T11:18:43Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)

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.