Cloud Heist at Warp Speed: How AI Supercharged an AWS Account Takeover

It started with a few stray credentials in a public bucket. Within minutes, it escalated into a full-blown cloud compromise, as attackers - armed with cutting-edge AI - stormed through Amazon Web Services (AWS) like seasoned digital burglars. This is not a hypothetical nightmare, but a chilling real-world breach that signals a new era in cybercrime: the age of AI-powered cloud takeovers.

Fast Facts

• Attackers used large language models (LLMs) to automate code-writing, reconnaissance, and decision-making during the breach.
• The initial entry point was exposed credentials in public AWS S3 buckets containing AI training data.
• Privilege escalation was achieved by exploiting AWS Lambda permissions, allowing attackers to overwrite functions and generate admin access keys.
• Attackers targeted 19 AWS principals, created backdoor admin accounts, and abused GPU resources for illicit AI training jobs.
• Sysdig’s detection relied on behavioral analytics, flagging rapid privilege jumps, mass reconnaissance, and suspicious resource usage.

The Anatomy of an AI-Driven Cloud Breach

The attackers’ journey began with a classic mistake: public S3 buckets containing sensitive credentials. These buckets, named after AI tools, exposed keys tied to an IAM user with the power to update AWS Lambda functions. But what set this breach apart wasn’t just the vulnerability - it was the speed and precision enabled by AI.

Large language models like Claude and Llama became digital accomplices, drafting reconnaissance scripts, parsing AWS policies, and suggesting next moves in real time. The attackers methodically probed AWS services: Secrets Manager, Lambda, EC2, RDS, and even specialized AI platforms like Bedrock and SageMaker. Each step was faster, more adaptive, and harder to detect than traditional attacks.

The pivotal moment came when the attackers rewrote a Lambda function - three times - until they struck gold: admin-level access. With Python code peppered with Serbian comments, they listed users, created new admin keys, and set up a “backdoor-admin” account. The automation was so slick that even the output included ready-to-use instructions for generating new credentials on demand.

Resource abuse followed. The attackers spun up a powerful GPU instance, installed AI libraries, and launched a public JupyterLab for remote access - potentially to train their own models or rent out stolen compute. Their scripts even uploaded instance details to S3 for later reconnection, and they rotated IPs and roles to evade detection.

Detection systems eventually caught up, flagging unusual Lambda updates, mass access key creation, and open security groups. But by then, the attackers had already mapped the environment, siphoned secrets, and demonstrated just how dangerous AI-assisted hacking has become.

Aftermath and Lessons Learned

This breach is a wake-up call for anyone managing cloud infrastructure. Least privilege access, strict controls on Lambda updates, vigilant monitoring, and hardening of public-facing resources are no longer optional. As attackers harness AI to move faster and smarter, defenders must adapt or risk being left in the dust.

WIKICROOK

• Large Language Model (LLM): A Large Language Model (LLM) is an AI trained to understand and generate human-like text, often used in chatbots, assistants, and content tools.
• AWS Lambda: AWS Lambda lets you run code in response to events, without managing servers. It’s cost-effective and ideal for scalable, event-driven applications.
• IAM (Identity and Access Management): IAM is a system that manages and controls who can access specific digital resources, ensuring only authorized users have the right permissions.
• S3 Bucket: An S3 Bucket is a secure online storage container in AWS used to store, organize, and manage files, data, and backups in the cloud.
• Privilege Escalation: Privilege escalation occurs when an attacker gains higher-level access, moving from a regular user account to administrator privileges on a system or network.