PromptLock represents a novel proof-of-concept ransomware that integrates artificial intelligence capabilities to enhance its malicious operations. Developed in the Go programming language, PromptLock targets multiple operating systems including Windows, Linux, and macOS, demonstrating cross-platform compatibility which broadens its potential attack surface. The ransomware leverages Lua scripts that are dynamically generated from hard-coded prompts by communicating with a locally hosted large language model (LLM) via the Ollama API. This AI-driven script generation allows the malware to adapt its behavior based on the infected system's environment, increasing its stealth and effectiveness. The malware conducts comprehensive filesystem scanning to identify sensitive information and selectively encrypts files using the SPECK 128-bit encryption algorithm in ECB mode. While SPECK is a lightweight cipher, its use in ECB mode is generally discouraged due to pattern leakage; however, the AI-driven adaptability and dynamic ransom note generation compensate by complicating detection and response efforts. PromptLock also dynamically generates ransom notes tailored to the infected machine, potentially improving the likelihood of ransom payment by providing contextually relevant instructions. The malware employs various tactics and techniques mapped to MITRE ATT&CK IDs such as T1033 (System Owner/User Discovery), T1547 (Boot or Logon Autostart Execution), T1204.002 (User Execution: Malicious File), and T1486 (Data Encrypted for Impact), indicating a sophisticated multi-stage attack lifecycle. Despite being a proof-of-concept and not currently observed in the wild, PromptLock highlights the emerging trend of AI-powered malware that can autonomously generate and execute malicious scripts, posing a significant challenge for traditional signature-based detection systems. The use of a locally hosted LLM reduces reliance on external command and control infrastructure, potentially evading network-based detection. This threat underscores the necessity for cybersecurity defenses to evolve in response to AI-enhanced adversarial techniques.

For European organizations, PromptLock's cross-platform nature means that enterprises running heterogeneous environments are at risk, including those using Windows, Linux, and macOS systems. The ransomware's ability to scan for sensitive files and encrypt them threatens confidentiality and availability of critical data, potentially disrupting business operations, causing financial losses, and damaging reputations. The dynamic generation of ransom notes tailored to the victim may increase the likelihood of ransom payment, further incentivizing attackers. Given Europe's strict data protection regulations such as GDPR, a successful ransomware attack could lead to significant regulatory penalties if personal data is compromised or lost. The AI-driven adaptability of PromptLock complicates detection and mitigation, increasing dwell time and potential damage. Additionally, sectors with high-value intellectual property or critical infrastructure components are particularly vulnerable to operational disruption and data loss. The lack of known exploits in the wild currently limits immediate impact, but the proof-of-concept status signals a potential future threat that could be weaponized by threat actors.

1. Implement advanced endpoint detection and response (EDR) solutions capable of behavioral analysis to detect AI-generated script execution and unusual filesystem scanning activities. 2. Employ strict application whitelisting and restrict execution of unauthorized Lua scripts and Go binaries, especially those communicating with local APIs such as Ollama. 3. Monitor and control access to local LLM hosting environments and APIs to prevent misuse by malware. 4. Enforce least privilege principles to limit the ransomware's ability to execute persistence mechanisms (e.g., T1547) and escalate privileges. 5. Regularly back up critical data with offline or immutable backups to enable recovery without paying ransom. 6. Conduct user awareness training focusing on the risks of executing unknown files and social engineering tactics, as user execution is a likely infection vector. 7. Utilize network segmentation to contain infections and limit lateral movement. 8. Maintain up-to-date threat intelligence feeds and integrate indicators of compromise (IOCs) such as the provided hashes into security monitoring tools. 9. Develop incident response plans that specifically address AI-powered malware scenarios, including forensic analysis of AI-generated artifacts. 10. Collaborate with AI and cybersecurity communities to stay informed about emerging AI-driven threats and defensive techniques.