One Exploit to Rule them All? On the Security of Drop-in Replacement and Counterfeit Microcontrollers
August 21, 2020 Β· Declared Dead Β· π WOOT @ USENIX Security Symposium
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Johannes Obermaier, Marc Schink, Kosma Moczek
arXiv ID
2008.09710
Category
cs.CR: Cryptography & Security
Citations
11
Venue
WOOT @ USENIX Security Symposium
Last Checked
3 months ago
Abstract
With the increasing complexity of embedded systems, the firmware has become a valuable asset. At the same time, pressure for cost reductions in hardware is imminent. These two aspects are united at the heart of the system, i.e., the microcontroller. It runs and protects its firmware, but simultaneously has to prevail against cheaper alternatives. For the very popular STM32F1 microcontroller series, this has caused the emergence of many competitors in the last few years who offer drop-in replacements or even sell counterfeit devices at a fraction of the original price. Thus, the question emerges whether the replacements are silicon-level clones and, if not, whether they provide better, equal, or less security. In this paper, we analyze a total of six devices by four manufacturers, including the original device, in depth. Via a low-level analysis, we identify all of them as being individually developed devices. We further put the focus on debug and hardware security, discovering several novel vulnerabilities in all devices, causing the exposure of the entire firmware. All of the presented vulnerabilities, including invasive ones, are on a Do it Yourself (DiY) level without the demand for a sophisticated lab -- thereby underlining the urgency for hardware fixes. To facilitate further research, reproduction, and testing of other devices, we provide a comprehensive description of all vulnerabilities in this paper and code for proofs-of-concepts online.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted