Vulnerability DatabaseRUSTSEC-2024-0363

RUSTSEC-2024-0363
Rust vulnerability analysis and mitigation

Overview

RUSTSEC-2024-0363 is a vulnerability in the SQLx Rust library that involves Binary Protocol Misinterpretation caused by Truncating or Overflowing Casts. The issue was discovered and reported on August 15, 2024, affecting SQLx versions 0.8.0 and earlier. The vulnerability exists in the way SQLx handles binary protocol encoding, particularly in the handling of large data values (Github Issue).

Technical details

The vulnerability stems from truncating casts in the SQLx codebase, specifically in the protocol handling code. For example, in sqlx-postgres/src/arguments.rs, there is a truncating cast: (self.len() - offset - 4) as i32. When encoding a value larger than 4GiB, this can cause the length prefix in the protocol to overflow, potentially leading to the server misinterpreting the rest of the string as binary protocol commands (Github Issue).

Impact

If exploited, this vulnerability could allow an attacker to manipulate the binary protocol interpretation, potentially leading to SQL injection at the protocol level. The issue requires malicious input at least 4 GiB in size to be exploitable (Github Issue).

Mitigation and workarounds

Applications should validate untrustworthy user input and reject any input over 4 GiB, or any input that could encode to a string longer than 4 GiB. Web application backends should consider adding middleware that limits the size of request bodies by default. The issue has been fixed in version 0.8.3-1, which returns an error instead of silently truncating values larger than allowed in the binary protocol (Debian Tracker, Github Issue).

Community reactions

The vulnerability was initially brought to attention through a presentation at DEFCON 32, highlighting the potential for SQL injection attacks at the protocol level. The SQLx maintainers responded promptly by implementing fixes and issuing a security advisory (Github Issue).

Additional resources


SourceThis report was generated using AI

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