Apache Spark, a widely used big data processing framework, has a cryptographic vulnerability identified as CVE-2025-55039 affecting versions before 3.4.4, 3.5.2, and 4.0.0. The issue arises when the network encryption feature (spark.network.crypto.enabled) is enabled but the encryption cipher (spark.network.crypto.cipher) is not explicitly configured. Under these conditions, Spark defaults to AES in CTR mode (AES/CTR/NoPadding), which provides confidentiality but lacks authentication. This absence of authenticated encryption means that an attacker capable of intercepting RPC traffic between Spark nodes can perform bit-flipping attacks on ciphertext, altering messages without detection. Such tampering can compromise the integrity of critical communications like heartbeat signals and application data, potentially causing workflow errors or data corruption. The vulnerability is categorized under CWE-347 (Improper Verification of Cryptographic Signature) and CWE-326 (Inadequate Encryption Strength). To remediate, users should explicitly configure the cipher to AES/GCM/NoPadding, which provides authenticated encryption, or enable SSL encryption (spark.ssl.enabled=true) to secure transport channels robustly. Although no exploits have been reported in the wild, the vulnerability poses a significant risk to the integrity of distributed Spark operations, especially in environments where network attackers can intercept traffic.

For European organizations relying on Apache Spark for big data analytics and distributed computing, this vulnerability could lead to undetected manipulation of inter-node communications. This compromises data integrity and may disrupt critical data processing workflows, potentially affecting decision-making and operational continuity. Industries such as finance, telecommunications, healthcare, and government agencies using Spark clusters could face risks of data corruption or workflow failures. The man-in-the-middle attack vector requires network access, so organizations with less secure internal networks or exposed cluster communications are at higher risk. The lack of authentication in encryption can also undermine compliance with European data protection regulations like GDPR, which mandate data integrity and security. Additionally, the potential for subtle data manipulation without detection could lead to erroneous analytics outcomes, impacting business intelligence and operational decisions.

European organizations should immediately audit their Apache Spark configurations to verify if spark.network.crypto.enabled is set to true and whether spark.network.crypto.cipher is explicitly configured. If encryption is enabled without specifying the cipher, they must update the configuration to use AES/GCM/NoPadding to ensure authenticated encryption. Alternatively, enabling SSL encryption by setting spark.ssl.enabled to true provides stronger transport security and should be preferred where feasible. Organizations should also plan to upgrade to patched Spark versions 3.4.4, 3.5.2, or 4.0.0 or later, which address this vulnerability. Network segmentation and strict access controls should be enforced to limit the possibility of man-in-the-middle attacks on RPC traffic. Monitoring for unusual network activity and integrity checks on Spark workflows can help detect exploitation attempts. Finally, integrating cryptographic best practices and regular security reviews into Spark deployment lifecycles will reduce future risks.