CVE-2025-8320 in Wall Connector
Summary
by MITRE • 07/30/2025
Tesla Wall Connector Content-Length Header Improper Input Validation Remote Code Execution Vulnerability. This vulnerability allows network-adjacent attackers to execute arbitrary code on affected installations of Tesla Wall Connector devices. Authentication is not required to exploit this vulnerability.
The specific flaw exists within the parsing of the HTTP Content-Length header. The issue results from the lack of proper validation of user-supplied data, which can result in memory access past the end of an allocated buffer. An attacker can leverage this vulnerability to execute code in the context of the device. Was ZDI-CAN-26300.
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Analysis
by VulDB Data Team • 07/30/2025
The CVE-2025-8320 vulnerability represents a critical remote code execution flaw in Tesla Wall Connector devices that demonstrates a fundamental failure in input validation mechanisms within the HTTP protocol handling stack. This vulnerability resides in the improper parsing of the Content-Length header, which serves as a critical control mechanism for HTTP request processing. The flaw stems from inadequate bounds checking and memory management practices during HTTP request processing, creating a pathway for attackers to manipulate device behavior through crafted HTTP requests. The vulnerability's severity is amplified by its network-adjacent attack vector, meaning that adversaries do not require physical access or authentication credentials to exploit the flaw, making it particularly dangerous in environments where these devices are deployed.
The technical root cause of this vulnerability aligns with CWE-129, which addresses improper validation of input boundaries, and CWE-787, which covers out-of-bounds write operations. The device's HTTP server implementation fails to properly validate the Content-Length header value against expected ranges and buffer limits, allowing maliciously crafted headers to trigger memory corruption. When the device processes an HTTP request with an oversized or malformed Content-Length value, the parsing logic attempts to allocate memory or access buffer regions beyond their allocated boundaries. This memory corruption can be leveraged to overwrite critical program execution structures, enabling attackers to inject and execute arbitrary code within the device's operational context. The vulnerability's exploitation requires only network access to the device's HTTP interface, eliminating the need for authentication or specialized physical access.
From an operational perspective, this vulnerability presents significant risk to electric vehicle charging infrastructure security, particularly in commercial and residential settings where Tesla Wall Connectors are deployed. The remote code execution capability allows attackers to gain full control over the device's operational functions, potentially enabling them to modify charging parameters, disable charging capabilities, or establish persistent access points. Attackers could leverage this vulnerability to disrupt charging services, potentially causing financial losses for businesses or inconvenience for consumers. The impact extends beyond individual device compromise, as compromised wall connectors could serve as entry points for broader network infiltration within facilities that rely on Tesla charging infrastructure. This vulnerability directly maps to ATT&CK technique T1210, which covers exploitation of remote services, and T1059, covering command and scripting interpreter usage for execution.
Mitigation strategies for CVE-2025-8320 should focus on immediate network segmentation and access controls to limit exposure to untrusted networks. Organizations should implement firewall rules that restrict access to the Wall Connector's HTTP interfaces to trusted administrative networks only, while also deploying network monitoring solutions to detect anomalous HTTP traffic patterns. The most effective long-term solution involves applying firmware updates from Tesla that address the specific input validation issues in the Content-Length header parsing logic. Security teams should also consider implementing intrusion detection systems that can identify crafted HTTP requests with suspicious Content-Length values. Additionally, network administrators should regularly audit device configurations and monitor for unauthorized access attempts. The vulnerability highlights the importance of secure coding practices, particularly around input validation and memory management, and demonstrates the critical need for regular security assessments of embedded systems in industrial control environments.