CVE-2026-80922 in Linux
Summary
by MITRE • 09/09/2026
In the Linux kernel, the following vulnerability has been resolved:
crypto: qcom-rng - Allow zero as a random number
Zero is a valid random number and needs to be allowed. Otherwise the output is distinguishable from random.
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Analysis
by VulDB Data Team • 09/09/2026
The Qualcomm Random Number Generator driver in the Linux kernel contained a logic flaw that incorrectly rejected zero as an invalid or non-random value during its validation process. This issue stems from a fundamental misunderstanding of cryptographic randomness properties, where any possible bit pattern within the output space is statistically valid and expected to occur with equal probability over time. By explicitly filtering out or rejecting outputs that evaluate to zero, the driver introduced a deterministic bias into the random number generation stream. In cryptography, true randomness requires uniform distribution across all potential values in the range; removing one specific value creates a distinguishable pattern that deviates from ideal statistical behavior. This deviation compromises the entropy quality provided by this hardware source, potentially weakening systems or applications that rely on high-quality randomness for security-critical operations such as key generation, nonce creation, or session token assignment.
From a technical perspective, this vulnerability is classified under CWE-330: Use of Insufficiently Random Values. The flaw resides in the validation logic within the qcom-rng driver code, which likely included an explicit check to ensure that generated numbers were not zero before passing them up the kernel stack or returning them to user space applications. While developers may have intended this as a safeguard against null pointers or undefined states, it fundamentally violates the principles of cryptographic pseudo-random number generation (CSPRNG). In hardware-based random number generators, every bit combination is equally likely unless there is evidence of hardware failure, and rejecting zero artificially reduces the entropy pool's effective size. This makes the output distinguishable from a truly uniform distribution, which can be exploited by attackers who perform statistical analysis on generated values to predict future outputs or reduce the search space for brute-force attacks against cryptographic keys derived using this source.
The operational impact of this vulnerability depends heavily on how the Qualcomm RNG is utilized within specific embedded systems and SoCs that rely on it as a primary entropy source. If applications use this driver directly for generating encryption keys, initialization vectors, or authentication tokens, the reduced randomness could lead to predictable security parameters. For instance, if an attacker can observe enough outputs from this generator, they might identify the bias toward non-zero values and potentially reconstruct parts of the internal state or predict subsequent random numbers. This is particularly dangerous in environments where hardware RNGs are trusted as high-entropy sources without sufficient supplementation from other entropy pools like /dev/random or software-based jitter entropy generators. The vulnerability does not typically allow for direct remote code execution but significantly degrades the security posture by weakening the foundation upon which cryptographic assurances are built.
To mitigate this risk, it is essential to ensure that systems running affected versions of the Linux kernel apply the upstream patch that removes the erroneous zero-checking logic from the qcom-rng driver. Administrators should verify their kernel version and update to a patched release as soon as possible. Additionally, organizations relying on Qualcomm hardware for cryptographic operations should audit their entropy sources to confirm they are not exclusively dependent on this single RNG without fallback mechanisms or additional entropy mixing via software-based generators like haveged or systemd-random-seed. Monitoring logs for unusual patterns in random number generation failures might also provide early indicators of exploitation attempts, although the primary defense remains patching the kernel to restore full statistical uniformity to the output stream. This fix aligns with industry best practices outlined by NIST SP 800-90A regarding entropy sources and ensures compliance with standards that require unbiased random number generation for cryptographic applications.