A meticulously documented cyberattack on an Amazon Web Services environment has sent a chilling message through the cybersecurity community, demonstrating that artificial intelligence is no longer a theoretical threat but a potent weapon that can compress a complex cloud intrusion into the time it takes to brew a pot of coffee. The incident, detailed by the Sysdig Threat Research Team, saw an attacker escalate from minimal access to full administrative control in under ten minutes, a feat made possible by leveraging a large language model (LLM) as a “force multiplier.” This event serves as a stark warning that the traditional time buffer defenders once relied upon to detect and respond to a breach is rapidly evaporating. The attack was not just a showcase of speed but a confluence of a fundamental security oversight and advanced, AI-driven techniques for reconnaissance, code generation, and lateral movement, signaling a paradigm shift in the cyber threat landscape.
The Initial Infiltration a Cascade of Failures
The entire sophisticated attack was predicated on a preventable, yet alarmingly common, security misconfiguration that provided the threat actor with their initial access. The attackers gained their first foothold by discovering valid, long-term access credentials that had been carelessly exposed in a public Simple Storage Service (S3) bucket. This fundamental error underscores a persistent theme in cybersecurity: even the most advanced attacks often begin by exploiting basic failures in security hygiene. The intruders further streamlined their discovery process by actively searching for S3 buckets that used naming conventions related to common AI tools, allowing them to locate the compromised credentials with alarming efficiency. This initial vector serves as a cautionary tale, reinforcing the critical need for organizations to adopt more secure practices, such as prioritizing the use of Identity and Access Management (IAM) roles with temporary credentials over static, long-term user keys, which must be diligently secured and rotated if their use is unavoidable.
A defining characteristic that set this breach apart was its unprecedented speed, which fundamentally alters the dynamics of cyber defense. The threat actor escalated from an account with minimal, read-only permissions to achieving full administrative control in a mere eight minutes. Researchers attribute this velocity directly to the use of LLMs, which effectively remove the hesitation and manual effort inherent in human-led attacks. The AI was instrumental in automating reconnaissance, generating malicious code on the fly, and facilitating rapid real-time decision-making throughout the attack chain. This acceleration collapses tasks that traditionally required hours of trial and error—such as enumerating permissions, testing for privilege escalation paths, and moving laterally across the network—into a single, continuous, and rapid sequence. The incident demands a significant shift in defensive strategies toward more automated and real-time detection and response capabilities capable of matching this new machine-speed threat.
Anatomy of an Automated Intrusion
Upon gaining initial access with a restrictive “ReadOnlyAccess” policy, the attacker immediately deployed their AI tool to elevate privileges within the AWS environment. They accomplished this through a technique known as Lambda function code injection, where the actor repeatedly replaced the code of an existing Lambda function named “EC2-init.” The script iterated three times, refining its malicious payload until it successfully targeted and compromised an administrative user account. The evidence pointing to LLM involvement in this phase was compelling and multifaceted. Forensic analysis revealed that the code was written in Serbian, featured unusually comprehensive comments, and included robust exception handling—qualities and a level of polish that strongly suggest automated generation rather than manual coding under pressure. The speed at which this sophisticated code was developed and deployed further solidified the conclusion that an AI was at the helm, performing complex tasks at a velocity that a human operator could not match.
Once administrative access was secured, the attacker moved laterally across 19 unique AWS principals, attempting to assume a wide array of roles, including cross-account roles, by enumerating account IDs. A peculiar and revealing aspect of this phase was the appearance of what researchers described as “AI hallucinations.” The attacker’s scripts attempted to access account IDs that did not belong to the victim organization, including two IDs with simplistic ascending and descending digit patterns. This behavior, which is inconsistent with a human attacker’s typical methodology, is a known pattern in AI models, which can sometimes generate erroneous or nonsensical data when tasked with generating sequences or identifiers. While this currently represents a limitation and a potential forensic clue, researchers predict that such hallucinations will become rarer as offensive AI agents grow more accurate and sophisticated, making detection even more challenging in the near future.
The New Prize Hijacking AI and GPU Resources
The breach was not merely about gaining access; the threat actor had clear, modern objectives centered on exploiting the victim’s valuable AI and GPU resources for their own gain. The attacker specifically targeted Amazon Bedrock, an AI application development environment, in an act described as “LLMjacking.” They programmatically invoked a diverse range of sophisticated AI models, including multiple versions of Anthropic’s Claude, Meta’s Llama 4 Scout, and Amazon’s Titan models. To accomplish this, the attacker used advanced techniques, such as programmatically interacting with AWS Marketplace APIs to accept usage agreements on the victim’s behalf. Furthermore, they employed cross-region inference profiles to distribute their activity and complicate detection, effectively turning the victim’s own advanced AI infrastructure into a tool for the attacker while racking up significant costs for the compromised organization.
After successfully exploiting the AI models, the actor pivoted to their secondary objective: provisioning powerful GPU instances on the Elastic Compute Cluster (EC2). The likely purpose of this was twofold, either to train their own custom LLMs using the victim’s expensive computational resources or to resell access to this high-demand processing power on underground markets. Once again, the training scripts used in this phase contained hallucinated elements, such as nonsensical parameters and file paths, which reinforced the conclusion that an LLM was a core component of the attacker’s toolkit from start to finish. This focus on hijacking computational resources for AI-related tasks marks a significant evolution in attacker motives, moving beyond simple data theft to the theft of high-value processing capabilities that power the modern digital economy.
A Paradigm Shift for Cloud Defense
This incident served as a stark confirmation that the combination of human error and AI-powered automation creates a highly volatile and dangerous threat scenario. The attack demonstrated that threat actors can now execute complex, multi-stage cloud intrusions in a timeframe that fundamentally challenges traditional security monitoring and incident response cycles. The primary trend identified by experts was the maturation of AI as a potent offensive weapon. While the initial breach was framed as a “stubborn refusal to master security fundamentals,” the subsequent AI-driven escalation heralded a new era of cyber threats. It became clear that defenders must now contend with adversaries who can operate at machine speed, requiring a corresponding evolution in defensive strategies to counter attacks that unfold in minutes, not hours or days. The consensus was that such attacks would become increasingly commonplace as AI hits a critical mass as both a threat enabler and a target.
In response, several key mitigation strategies were emphasized to combat this evolved threat. The entire attack could have been prevented by properly securing S3 buckets and adhering to credential management best practices, which remains the most critical line of defense. However, given the velocity of these intrusions, organizations must also implement robust runtime detection and response capabilities that can identify and neutralize threats in real time. Enforcing the principle of least privilege is essential to limit the potential damage an attacker can inflict if an account is compromised. An Amazon spokesperson clarified that AWS services and infrastructure were not at fault and operated as designed; the incident stemmed from a customer’s misconfiguration. Therefore, the responsibility falls on organizations to secure their resources and utilize monitoring services to mitigate the risk of such rapid, unauthorized activity.
