physical-bitcoin-attacks

Known Physical Bitcoin Attacks A list of known attacks against Bitcoin / crypto asset owning entities that occurred in meatspace. NOTE: this list is not comprehensive; many attacks are not publicly reported. If you are aware of an attack that is not listed, please open an issue or pull request —...

TEE Anchor: Cross-TEE Organizational Endorsement for Mitigating TEE Physical Attacks

In 2025, practical physical-access attacks against TEEs, such as TEE.fail and Battering RAM, were disclosed, posing a serious threat to current TEEs. The more strictly the target machine is guarded, the harder such attacks are to mount. Residing under trusted management has therefore emerged as a...

CVE-2026-21073

Improper input validation in Galaxy Themes prior to SMR Aug-2026 Release 1 allows physical attackers to launch arbitrary activity...

AdaptoNet: Modular Foundation-Adaptive Neural Networks for Cyber-Physical Attack Detection in Power Grids

Cyber attacks on the power grid combine physical disruptions with compromised data to destabilize cyber-physical systems. We demonstrate that data denial attacks, where adversaries block measurements in a targeted region while triggering a line outage, reduce detection performance by more than...

physical-bitcoin-attacks — Updated!

Known Physical Bitcoin Attacks A list of known attacks against Bitcoin / crypto asset owning entities that occurred in meatspace. NOTE: this list is not comprehensive; many attacks are not publicly reported. If you are aware of an attack that is not listed, please open an issue or pull request. F...

Beetel 777VR1 Access Control Vulnerability

Beetel 777VR1 is a router produced by the Beetel company. Versions of Beetel 777VR1 prior to 01.00.09/01.00.0955 contain a vulnerability related to access control. This vulnerability stems from improper access control in the UART interface, which could lead to physical device attacks...

EUVD-2015-1984

Malware in sbrugna...

EUVD-2017-17792

Malware in sbrugna...

EUVD-2020-5719

Malware in sbrugna...

EUVD-2006-4967

Malware in sbrugna...

EUVD-2015-3084

Malware in sbrugna...

EUVD-2020-5718

Malware in sbrugna...

EUVD-2014-4359

Malware in sbrugna...

EUVD-2013-4720

Malware in sbrugna...

EUVD-2024-43430

Malicious code in bioql PyPI...

EUVD-2023-35040

Malicious code in bioql PyPI...

EUVD-2024-18609

Malicious code in bioql PyPI...

EUVD-2023-47008

Malicious code in bioql PyPI...

Beyond Vulnerabilities: a Survey of Adversarial Attacks As Both Threats and Defenses in Computer Vision Systems

Adversarial attacks against computer vision systems have emerged as a critical research area that challenges the fundamental assumptions about neural network robustness and security. This comprehensive survey examines the evolving landscape of adversarial techniques, revealing their dual nature a...

In-Context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems

Recent advances in biometric systems have significantly improved the detection and prevention of fraudulent activities. However, as detection methods improve, attack techniques become increasingly sophisticated. Attacks on face recognition systems can be broadly divided into physical and digital...