A recent Threatsday bulletin highlights two critical developments in cybersecurity: the accelerating shift toward Post-Quantum Cryptography (PQC) and the discovery of emerging vulnerabilities in AI-driven systems. As organizations prepare for quantum-era threats, attackers are increasingly targeting AI models and infrastructures, exposing new and complex risk surfaces across industries. The bulletin underscores the urgency of adopting PQC standards due to the anticipated capabilities of quantum computers to break traditional encryption methods such as RSA and ECC. Governments and enterprises are being urged to begin transitioning to quantum-resistant algorithms to safeguard sensitive data against future decryption threats, particularly “harvest now, decrypt later” attacks. Simultaneously, the report identifies a rise in vulnerabilities affecting AI systems, including prompt injection, data poisoning, and model inversion attacks. These flaws exploit weaknesses in how AI models process untrusted inputs or handle training data. For example, prompt injection can manipulate large language models into bypassing safeguards, while data poisoning compromises model integrity during training phases. Additionally, insecure AI pipelines and lack of validation controls increase exposure to adversarial manipulation. The convergence of PQC transition challenges and AI-specific threats signals a dual-front security concern requiring immediate attention from defenders.
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A financial institution in South Asia was recently targeted in a coordinated cyberattack involving two custom malware strains, BRUSHWORM and BRUSHLOGGER. These tools were deployed ...