In the rapidly expanding universe of artificial intelligence, the very mechanisms designed to streamline development and deployment are now being exposed as sophisticated pathways for catastrophic security breaches. A recent extensive analysis of Google’s Vertex AI platform has uncovered significant privilege-escalation vulnerabilities, but the response from the tech giant has sent a chill through the cybersecurity community. The issue, according to Google, is not a bug to be fixed but a feature that is “working as intended,” a declaration that fundamentally shifts the burden of security from the multi-trillion-dollar cloud provider to its enterprise customers.
This revelation, stemming from research by cybersecurity firm XM Cyber, challenges the core assumption that managed cloud services are inherently secure. The findings detail how default configurations in Vertex AI, a platform central to many organizations’ AI strategies, create a direct line for an attacker with minimal access to seize control of highly privileged accounts. This situation forces a critical re-evaluation of the trust placed in the automated systems that underpin the AI revolution, highlighting a growing disconnect between vendor design philosophy and enterprise security reality.
When a Backdoor Becomes the Front Door
The central paradox of modern cloud platforms is that their greatest strengths—automation and seamless integration—can also be their most profound weaknesses. The convenience of a managed AI service like Vertex AI lies in its ability to handle complex background processes autonomously, freeing up developers to focus on building models. However, this convenience is powered by incredibly privileged system identities known as Service Agents. These automated accounts are granted broad permissions across a project to ensure services can function without constant human intervention.
This architectural choice creates an environment where the most powerful credentials are not held by humans but by the platform itself. The research from XM Cyber demonstrated that these automated agents can be co-opted. An attacker who gains even a low-level foothold, such as a “Viewer” role, can manipulate the system to hijack the access token of a Service Agent. In doing so, they transform a trusted, automated process into a “double agent,” using the platform’s own sanctioned power to move laterally, access sensitive data, and establish a persistent, hard-to-detect presence within an organization’s cloud environment.
The “Working as Intended” Paradox
At the heart of the vulnerability lies a straightforward, repeatable process of privilege escalation. The mechanism begins with an actor possessing minimal permissions within a Google Cloud project. By exploiting the inherent trust between Vertex AI and its underlying infrastructure, this actor can trick the platform into revealing the access token for the AI Platform Service Agent. This automated identity, by default, possesses extensive editor-level privileges, giving it sweeping control over project resources. Google’s response that this is “working as intended” suggests that this powerful, hijackable agent is a necessary component of the platform’s design, placing the responsibility squarely on the customer to build custom safeguards around it.
This stance is particularly alarming because it reframes a significant security risk as a user configuration problem. Security experts argue this absolves the provider of the responsibility to design a secure-by-default environment. For an enterprise, the discovery means that relying on vendor-provided roles and permissions is insufficient. Organizations must now treat these automated Service Agents as their most privileged users, subjecting them to the same scrutiny and monitoring as a lead system administrator, a task for which many security teams are unprepared.
Not an Isolated Incident
This “by design” defense is not a novel strategy but part of a troubling pattern across the cloud industry. Major providers are increasingly using the shared responsibility model as a liability shield for insecure default settings. This approach redefines their security obligations, pushing the onus of securing complex, often poorly documented, internal service interactions onto the customer. This trend forces organizations to become experts in the intricate internal workings of cloud platforms, a significant and often unexpected operational burden.
The Vertex AI issue finds echoes in similar incidents across the cloud landscape. Security researchers have previously identified comparable privilege escalation paths in AWS SageMaker and Azure Storage, only to be met with similar responses that the platforms were “operating as expected.” Rock Lambros, CEO of RockCyber, connects this pattern directly to the “Identity and Privilege Abuse” category in the OWASP Agentic Top 10 list, a new framework for AI-specific security risks. This recurring theme underscores an urgent message for corporate leaders: the assumption that a “managed” service is a “secured” service is a dangerous misconception that leaves enterprises exposed.
Cloaked in Legitimacy
The most insidious aspect of this type of vulnerability is its ability to evade traditional security monitoring. As Sanchit Vir Gogia, chief analyst at Greyhound Research, explains, the convenience of the platform creates dangerous blind spots. When an attacker hijacks a Service Agent, their malicious actions—whether accessing sensitive data in Google Cloud Storage or running unauthorized queries in BigQuery—appear as legitimate, platform-sanctioned activity. Security tools configured to track human user behavior are typically blind to these operations, as they are masked by the identity of a trusted internal service.
This “invisible risk” is amplified by the insider threat. A malicious employee with even minimal access could exploit this flaw to quietly grant themselves sweeping permissions, making it exceptionally difficult to trace the origin of a breach. Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, notes that a compromised insider could leverage these built-in weaknesses to devastating effect. The structural nature of this risk is further highlighted by previous reports from Palo Alto Networks, which identified similar vulnerabilities in Vertex AI months earlier. The re-emergence of these issues suggests a deep-seated architectural problem rather than a simple, patchable bug.
From Reactive to Proactive Defense
Given that cloud vendors are unlikely to redesign their core architecture in the short term, enterprises cannot afford a reactive posture. The strategic imperative has shifted toward building “compensating controls” to mitigate risks that providers deem acceptable. Waiting for a vendor to change its definition of “intended behavior” is not a viable security strategy. Instead, organizations must proactively implement their own layers of defense tailored to the unique threats posed by automated service identities.
The most critical step, according to experts, is to treat Service Agents with the same rigor as the most privileged human administrators. This requires a fundamental shift in monitoring strategy. Security teams must now focus on baselining the normal behavior of these automated agents and building high-fidelity alerts for any deviation. This includes monitoring for anomalous activities such as unusual query patterns, access to new or unexpected storage buckets, or changes in API call frequency. By focusing detection on the behavior of these agents, rather than just their identity, organizations can begin to unmask an attacker hiding in plain sight.
The revelations surrounding Vertex AI’s design represent a pivotal moment for cloud security. The incident moved beyond a discussion of a single vulnerability and forced a broader industry reckoning with the nature of shared responsibility in the age of AI. It was a clear signal that the convenience of managed services came with hidden costs and that enterprises had to fundamentally reassess their trust in the default security postures of their cloud providers. This event underscored the critical need for a new security paradigm, one where organizations took ownership of monitoring every identity, human or automated, within their environment, recognizing that the greatest threats could come from the very tools designed to help them succeed.
