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4. Malicious Actors & Misuse2 - Post-deployment

Offensive Cyber Operations (General)

Offensive cyber operations are malicious attacks on computer systems and networks aimed at gaining unauthorized access to, manipulating, denying, disrupting, degrading, or destroying the target system. These attacks can target the system’s network, hardware, or software. Advanced AI assistants can be a double-edged sword in cybersecurity, benefiting both the defenders and the attackers. They can be used by cyber defenders to protect systems from malicious intruders by leveraging information trained on massive amounts of cyber-threat intelligence data, including vulnerabilities, attack patterns, and indications of compromise. Cyber defenders can use this information to enhance their threat intelligence capabilities by extracting insights faster and identifying emerging threats. Advanced cyber AI assistant tools can also be used to analyze large volumes of log files, system output, or network traffic data in the event of a cyber incident, and they can ask relevant questions that an analyst would typically ask. This allows defenders to speed up and automate the incident response process. Advanced AI assistants can also aid in secure coding practices by identifying common mistakes in code and assisting with fuzzing tools. However, advanced AI assistants can also be used by attackers as part of offensive cyber operations to exploit vulnerabilities in systems and networks. They can be used to automate attacks, identify and exploit weaknesses in security systems, and generate phishing emails and other social engineering attacks. Advanced AI assistants can also be misused to craft cyberattack payloads and malicious code snippets that can be compiled into executable malware files.

Source: MIT AI Risk Repositorymit377

ENTITY

1 - Human

INTENT

1 - Intentional

TIMING

2 - Post-deployment

Risk ID

mit377

Domain lineage

4. Malicious Actors & Misuse

223 mapped risks

4.2 > Cyberattacks, weapon development or use, and mass harm

Mitigation strategy

1. Establish a comprehensive AI Governance Framework (e.g., NIST AI RMF) to integrate repeatable risk assessments and threat modeling across the AI development lifecycle, ensuring a formal structure for managing security and accountability. 2. Conduct continuous adversarial testing and AI red teaming exercises to proactively stress-test model and system resilience against automated exploitation, vulnerability discovery, and emerging attack vectors, utilizing updated AI attack intelligence. 3. Deploy advanced, integrated security solutions, specifically combining Network Detection and Response (NDR) with Endpoint Detection and Response (EDR), to enable 24/7 continuous monitoring and rapid detection of behavioral anomalies and adversarial activity operating at machine speed.