This threat describes a highly automated and AI-assisted intrusion campaign targeting AWS cloud environments. The attacker leveraged publicly exposed credentials found in unsecured S3 buckets to gain initial access. Following this, rapid privilege escalation was achieved by injecting malicious code into AWS Lambda functions, allowing the attacker to execute arbitrary code with elevated permissions. The adversary then performed lateral movement across 19 distinct AWS principals, exploiting role chaining to escalate privileges further and evade detection. A notable aspect of the attack is the abuse of Amazon Bedrock, a managed service for large language models (LLMs), to perform 'LLMjacking'—leveraging the AI platform itself for malicious purposes, possibly to generate or automate attack code and decision-making. The attacker also launched GPU instances, likely to train or run malicious AI models, indicating resource abuse. The campaign included extensive reconnaissance, data exfiltration, and persistence mechanisms, with IP rotation used to obfuscate the attack origin. The use of AI-generated code and automation significantly reduced the time to achieve administrative access to under 10 minutes. The attack techniques map to MITRE ATT&CK tactics such as credential access (T1078), privilege escalation (T1069), lateral movement (T1136), and defense evasion (T1562.001). Although no CVE or known exploits are associated, the sophistication and speed of this attack highlight emerging risks in cloud security, especially with AI integration.

For European organizations, this threat poses a substantial risk to cloud infrastructure confidentiality, integrity, and availability. Unauthorized administrative access can lead to full control over AWS environments, enabling data theft, service disruption, and resource abuse such as cryptomining or malicious AI model training. The use of AI to automate and accelerate attacks reduces response time and increases the likelihood of successful compromise. Organizations relying on Amazon Bedrock and GPU instances are particularly vulnerable to resource hijacking and data leakage. Given the widespread adoption of AWS across Europe, including critical sectors like finance, healthcare, and government, the impact could extend to sensitive personal data breaches and operational disruptions. The attack's rapid escalation and persistence attempts complicate detection and remediation, potentially leading to prolonged exposure. Furthermore, the abuse of AI services introduces novel risks that traditional cloud security controls may not fully address, necessitating updated defense strategies.

1. Enforce strict access controls and audit policies for S3 buckets to prevent credential exposure; implement automated scanning for public credentials. 2. Harden Lambda function permissions by applying the principle of least privilege and regularly reviewing function code for unauthorized changes or injections. 3. Monitor AWS principals and role usage for unusual lateral movement or role chaining activities using AWS CloudTrail and GuardDuty. 4. Implement anomaly detection for Amazon Bedrock usage and GPU instance launches to identify potential abuse or unauthorized AI model training. 5. Employ multi-factor authentication (MFA) for all privileged accounts and rotate credentials frequently. 6. Use AI-enhanced security tools to detect AI-generated attack patterns and automate response workflows. 7. Establish comprehensive logging and alerting for IP rotation and suspicious network activities. 8. Conduct regular penetration testing and red teaming exercises simulating AI-assisted attacks to evaluate defenses. 9. Educate cloud administrators on emerging AI-related threats and secure coding practices for serverless functions. 10. Integrate threat intelligence feeds to stay updated on evolving AI-assisted cloud intrusion tactics.