Alvise de Faveri Tron

dblp:329/6265 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2025
0000-0002-6249-4865ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 3 · 2 first-author · 3 since 2021
YearPublicationVenuePosition
2025 Phantom Trails: Practical Pre-Silicon Discovery of Transient Data Leaks
Alvise de Faveri Tron, Raphael Isemann, Hany Ragab, Cristiano Giuffrida, Klaus von Gleissenthall, Herbert Bos
USENIX Security Symposium1
2024 InSpectre Gadget: Inspecting the Residual Attack Surface of Cross-privilege Spectre v2
Sander Wiebing, Alvise de Faveri Tron, Herbert Bos, Cristiano Giuffrida
USENIX Security Symposium2
2022 CANflict: Exploiting Peripheral Conflicts for Data-Link Layer Attacks on Automotive Networks
abstract
Current research in the automotive domain has proven the limitations of the Controller Area Network (CAN) protocol from a security standpoint. Application-layer attacks, which involve the creation of malicious packets, are deemed feasible from remote but can be easily detected by modern Intrusion Detection Systems (IDSs). On the other hand, more recent link-layer attacks are stealthier and possibly more disruptive but require physical access to the bus. In this paper, we present CANflict, a software-only approach that allows reliable manipulation of the CAN bus at the data link layer from an unmodified microcontroller, overcoming the limitations of state-of-the-art works. We demonstrate that it is possible to deploy stealthy CAN link-layer attacks from a remotely compromised ECU, targeting another ECU on the same CAN network. To do this, we exploit the presence of pin conflicts between microcontroller peripherals to craft polyglot frames, which allows an attacker to control the CAN traffic at the bit level and bypass the protocol's rules. We experimentally demonstrate the effectiveness of our approach on high-, mid-, and low-end microcontrollers, and we provide the ground for future research by releasing an extensible tool that can be used to implement our approach on different platforms and to build CAN countermeasures at the data link layer.
Alvise de Faveri Tron, Stefano Longari, Michele Carminati, Mario Polino, Stefano Zanero
CCS1