Mathias Oberhuber (Graz University of Technology), Martin Unterguggenberger (Graz University of Technology), Lukas Maar (Graz University of Technology), Andreas Kogler (Graz University of Technology), Stefan Mangard (Graz University of Technology)

Software-based power side-channel attacks are a significant security threat to modern computer systems, enabling adversaries to extract confidential information. Existing attacks typically exploit direct power signals from dedicated interfaces, as demonstrated in the PLATYPUS attack, or power-dependent timing variations, as in the case of the Hertzbleed attack. As access to direct power signals is meanwhile restricted on more and more platforms, an important question is whether other exploitable power-related signals exist beyond timing proxies.

In this paper, we show that Android mobile devices expose numerous power-related signals that allow power side-channel attacks. We systematically analyze unprivileged sensors provided by the Android sensor framework on multiple devices and show that these sensors expose parasitic influences of the power consumption. Our results include new insights into Android sensor leakage, particularly a novel leakage primitive: the rotation dependent power leakage of the geomagnetic rotation vector sensor. We extensively evaluate the exposed sensors for different information leakage types. We compare them with the corresponding ground truth, achieving correlations greater than 0.9 for some of our tested sensors. In extreme cases, we observe not only statistical results but also, e.g., changes in a compass app’s needle by approximately 30° due to CPU stress. Additionally, we evaluate the capabilities of our identified leakage primitives in two case studies: As a remote attacker via the Google Chrome web browser and as a local attacker running inside an installed app. In particular, we present an end-to-end pixel-stealing attack on different Android devices that effectively circumvents the browser’s cross-origin isolation with a leakage rate of 5 - 10 s per pixel. Lastly, we demonstrate a proof-of-concept AES attack, leaking individual key bytes using our newly discovered leakage primitive.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 118 ) ) ) [post__not_in] => Array ( [0] => 19977 ) )

RContainer: A Secure Container Architecture through Extending ARM CCA...

Qihang Zhou (Institute of Information Engineering, Chinese Academy of Sciences), Wenzhuo Cao (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyberspace Security, University of Chinese Academy of Sciences), Xiaoqi Jia (Institute of Information Engineering, Chinese Academy of Sciences), Peng Liu (The Pennsylvania State University, USA), Shengzhi Zhang (Department of Computer Science, Metropolitan College,…

Read More

Non-intrusive and Unconstrained Keystroke Inference in VR Platforms via...

Tao Ni (City University of Hong Kong), Yuefeng Du (City University of Hong Kong), Qingchuan Zhao (City University of Hong Kong), Cong Wang (City University of Hong Kong)

Read More

PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

Zizhi Jin (Zhejiang University), Qinhong Jiang (Zhejiang University), Xuancun Lu (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Read More

Towards LLM-Assisted Vulnerability Detection and Repair for Open-Source 5G...

Rupam Patir (University at Buffalo), Qiqing Huang (University at Buffalo), Keyan Guo (University at Buffalo), Wanda Guo (Texas A&M University), Guofei Gu (Texas A&M University), Haipeng Cai (University at Buffalo), Hongxin Hu (University at Buffalo)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)