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CVE-2025-32056: CWE-1241: Use of Predictable Algorithm in Random Number Generator in Bosch Infotainment system ECU

0
Medium
VulnerabilityCVE-2025-32056cvecve-2025-32056cwe-1241
Published: Thu Jan 22 2026 (01/22/2026, 15:21:21 UTC)
Source: CVE Database V5
Vendor/Project: Bosch
Product: Infotainment system ECU

Description

The anti-theft protection mechanism can be bypassed by attackers due to weak response generation algorithms for the head unit. It is possible to reveal all 32 corresponding responses by sniffing CAN traffic or by pre-calculating the values, which allow to bypass the protection. First identified on Nissan Leaf ZE1 manufactured in 2020.

AI-Powered Analysis

AILast updated: 01/22/2026, 15:51:00 UTC

Technical Analysis

CVE-2025-32056 identifies a security vulnerability in the Bosch Infotainment system ECU, specifically related to the anti-theft protection mechanism implemented in vehicles like the Nissan Leaf ZE1 manufactured in 2020. The root cause is the use of a predictable algorithm in the random number generator responsible for generating challenge-response pairs used to authenticate commands on the vehicle's Controller Area Network (CAN) bus. The anti-theft mechanism relies on 32 possible responses, all of which can be revealed either by passively sniffing CAN traffic or by pre-calculating the response values due to the weak randomness. This predictability allows an attacker with access to the CAN bus to bypass the anti-theft protection, potentially gaining unauthorized control or access to vehicle functions protected by this mechanism. The vulnerability affects the confidentiality and integrity of the vehicle's security systems but does not impact availability. The CVSS v3.1 score is 4.0 (medium), reflecting that exploitation requires physical proximity (attack vector: physical), no privileges or user interaction, but the scope is changed as the attacker can affect components beyond the initially vulnerable ECU. No patches have been publicly released yet, and no known exploits are reported in the wild. The vulnerability is categorized under CWE-1241, which relates to the use of predictable algorithms in random number generation, a common cryptographic weakness. The affected version is identified as 283C30861E of the Bosch Infotainment system ECU software.

Potential Impact

For European organizations, particularly automotive manufacturers, suppliers, and fleet operators, this vulnerability poses a risk of unauthorized access to vehicle systems protected by the anti-theft mechanism. Attackers could bypass security controls, potentially enabling theft, unauthorized vehicle control, or manipulation of infotainment and related vehicle functions. This undermines the confidentiality and integrity of vehicle security systems, which could lead to reputational damage, financial loss, and safety risks. The impact is heightened in Europe due to the widespread adoption of electric vehicles like the Nissan Leaf and the presence of Bosch as a major automotive supplier. Additionally, regulatory requirements in Europe emphasize vehicle cybersecurity, so exploitation could lead to compliance issues. While the vulnerability does not directly affect vehicle availability, the potential for unauthorized access to vehicle systems could indirectly impact operational continuity and user safety.

Mitigation Recommendations

Given the absence of publicly available patches, European organizations should implement several practical mitigations: 1) Engage with Bosch and vehicle manufacturers to obtain and deploy firmware updates as soon as they become available. 2) Implement network segmentation within the vehicle's CAN bus architecture to isolate critical security functions and limit attacker lateral movement. 3) Deploy anomaly detection systems on CAN traffic to identify unusual patterns indicative of sniffing or replay attacks. 4) Restrict physical access to vehicle diagnostic ports and CAN interfaces to prevent unauthorized connection. 5) Educate vehicle operators and maintenance personnel about the risks of connecting unauthorized devices to the vehicle network. 6) Collaborate with automotive cybersecurity specialists to conduct penetration testing and vulnerability assessments focused on the infotainment and ECU systems. 7) Monitor threat intelligence feeds for emerging exploits targeting this vulnerability to enable rapid response.

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Technical Details

Data Version
5.2
Assigner Short Name
ASRG
Date Reserved
2025-04-03T15:32:43.280Z
Cvss Version
3.1
State
PUBLISHED

Threat ID: 697243dd4623b1157c78ded8

Added to database: 1/22/2026, 3:35:57 PM

Last enriched: 1/22/2026, 3:51:00 PM

Last updated: 2/5/2026, 3:22:52 AM

Views: 50

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