Computer Science · Artificial Intelligence
Researchers developed a hardware-assisted system intended to detect message injection, spoofing, flooding and impersonation attacks on automotive networks. In tests using simulated traffic and a prototype automotive processor system, it reported an average detection time of 14 microseconds and less than 2 percent CPU overhead.
Researchers report that instructions hidden in images can redirect a vision-capable AI system to an attacker’s chosen task. In their analysis, GPT-4V followed the altered instructions in 15.8% of attacks.
A full-round analysis of the SCAN-C cipher found that its unusual XNOR key mixing adds no security compared with XOR. The researchers also report that the specified 12-round design can be attacked in an analysis requiring around 2^55 chosen plaintexts and 2^69 encryptions.
