CVE-2024-25389 identifies a cryptographic weakness in the RT-Thread real-time operating system's random number generator (RNG) implementation up to version 5.0.2. The RNG uses a linear congruential generator (LCG) defined by the formula: seed = 214013L * seed + 2531011L; return (seed >> 16) & 0x7FFF;. This algorithm is known to produce predictable sequences of pseudo-random numbers, which violates the requirements for cryptographic randomness. The vulnerability is classified under CWE-338 (Use of Cryptographically Weak Pseudo-Random Number Generator). The CVSS 3.1 base score is 7.5 (high), reflecting that the vulnerability can be exploited remotely (AV:N), with low attack complexity (AC:L), no privileges required (PR:N), no user interaction (UI:N), and impacts confidentiality (C:H) without affecting integrity or availability. Predictable random numbers can allow attackers to infer sensitive data, compromise cryptographic keys, or bypass security controls relying on randomness. Although no public exploits have been reported, the weakness poses a significant risk in embedded and IoT devices running RT-Thread, which are often deployed in critical systems. The lack of a patch link indicates that remediation may require custom updates or vendor intervention. This vulnerability highlights the importance of using secure RNGs in embedded systems to maintain confidentiality and overall security posture.

For European organizations, especially those deploying RT-Thread in embedded systems, IoT devices, or industrial control systems, this vulnerability can lead to severe confidentiality breaches. Predictable random numbers can compromise cryptographic keys, authentication tokens, or session identifiers, enabling attackers to decrypt sensitive communications or impersonate legitimate devices. This is particularly critical in sectors like manufacturing, energy, transportation, and healthcare, where embedded devices are integral to operations and safety. The vulnerability does not affect integrity or availability directly but can facilitate further attacks that do. Since exploitation requires no privileges or user interaction and can be performed remotely, the attack surface is broad. The impact is amplified in environments where RT-Thread devices handle sensitive data or control critical infrastructure. Organizations may face regulatory and compliance risks if data confidentiality is compromised. The absence of known exploits provides a window for proactive mitigation, but the high severity score demands urgent attention.

1. Replace the weak linear congruential random number generator with a cryptographically secure pseudorandom number generator (CSPRNG), such as those based on hardware entropy sources or well-vetted algorithms like Fortuna or ChaCha20-based RNGs. 2. Apply vendor patches or updates as soon as they become available; if none exist, consider custom firmware updates to address the RNG implementation. 3. Audit all cryptographic operations and security mechanisms relying on the RNG to ensure they are not vulnerable to prediction attacks. 4. Implement additional entropy sources where possible, such as hardware random number generators or environmental noise, to strengthen randomness. 5. Monitor network traffic and device behavior for anomalies that could indicate exploitation attempts targeting predictable RNG outputs. 6. For critical deployments, consider isolating affected devices or limiting their network exposure until mitigations are in place. 7. Engage with RT-Thread maintainers or vendors to prioritize a secure RNG implementation and timely patch releases. 8. Educate development and security teams on the importance of secure randomness in embedded systems to prevent similar issues.