Fact check: Does Oracle Corporation pose any privacy risks to users of their services? Would databases stored on their Virtual Machines be vulnerable to data scraping?

1. Why the recent zero-days matter: active exploitation and immediate privacy risk

A critical zero-day in Oracle E‑Business Suite (CVE-2025-61882) was remotely exploitable without authentication and has been used by the Clop ransomware gang in data theft, demonstrating the real-world privacy impact of unpatched enterprise software ^{[1] [2]}. Oracle issued an emergency update and indicators of compromise, signaling the vulnerability’s severity and active exploitation. The presence of a CVSS 9.8 score and Oracle’s own security advisory urging rapid patching indicate that databases accessible via affected E‑Business Suite components could be exfiltrated if exposed to the Internet without timely remediation ^{[5] [2]}.

2. Virtual machines are not inherently safe: VM-related flaws create escalation paths

Oracle VM/VirtualBox contained a high-severity vulnerability (CVSS 8.2) enabling privileged attackers to compromise host systems and potentially pivot into guest virtual machines, which could result in takeover scenarios for hosted workloads ^[3]. VM escapes or hypervisor/virtualization-layer compromises mean that even data stored within ostensibly isolated virtual machines can be exposed if the underlying platform is vulnerable. Historical incidents such as the #AttachMe flaw in Oracle Cloud show that cloud-edge and virtualization issues can rapidly translate to customer data exposure if not patched—a pattern that repeats unless operators maintain aggressive patching and segmentation ^{[6] [3]}.

3. Data scraping risk: technical exposure plus policy choices create the threat vector

Databases on Oracle-hosted VMs are vulnerable to data scraping when technical misconfigurations or unpatched vulnerabilities provide adversaries network access, or when attackers obtain privileged execution via zero-days and ransomware gangs. The Clop incidents and VM vulnerabilities demonstrate the technical path to large-scale scraping or theft ^{[1] [3] [2]}. Separately, Oracle’s initiative to vectorize customer data for AI introduces a non-technical scraping-like risk: broad data collection and transformation without explicit user consent can enable analytics and model-sourced exposure even where direct exfiltration hasn’t occurred ^[4].

4. Corporate practices and AI ambitions raise governance and consent questions

Oracle’s stated push to vectorize customer data for AI has raised concerns that the company is aggregating and processing large datasets without clearly defined consent mechanisms, increasing policy-level privacy risk beyond purely technical breaches ^[4]. While Oracle frames these efforts as product improvements and personalization, the scale and persistence of vectorized representations can enable downstream reuse, model inversion, or secondary disclosures if governance, retention, and access controls are not tightened. Moody’s warning about financial concentration does not directly prove privacy harm but highlights how strategic bets and resource allocation can shape risk management priorities ^[7].

5. Oracle’s proven patch response and compliance posture offer partial reassurance

Oracle has demonstrated the capacity to issue emergency patches quickly—addressing both the E‑Business Suite zero-day and prior cloud vulnerabilities like #AttachMe—indicating an operational ability to remediate once issues are disclosed ^{[1] [6]}. Oracle Cloud’s listing in the CSA STAR Registry (Level 1) reflects an adherence to baseline security and privacy self-assessment practices, offering some governance transparency ^[8]. However, self-reported compliance and rapid patching do not eliminate windows of exposure or policy-level concerns about data use; customers remain responsible for configuration, isolation, and contractual safeguards.

6. Practical mitigation steps for customers who host databases on Oracle VMs

Customers should assume a dual-threat model: technical exploits enabling data theft, and policy-level reuse of data for AI purposes. Immediate steps include urgent patching of disclosed CVEs, restrictive network exposure of management interfaces, MFA and least-privilege for service accounts, encryption at rest and in transit, and contractual limits on data use for AI/analytics. Oracle advisories emphasize swift updates for specific CVEs; combining those technical fixes with stricter contractual language around data vectorization and model training will materially reduce both scraping and second-order privacy risks ^{[5] [4]}.

7. Bottom line: risk exists but is manageable with technical and contractual controls

The evidence shows that Oracle services have had exploitable vulnerabilities leading to real data theft and that corporate data practices around AI raise consent and governance concerns. These constitute concrete privacy risks for users of Oracle services and for databases on their VMs ^{[1] [2] [4]}. Organizations can manage these risks through rapid patching, rigorous configuration and isolation, and explicit contractual protections on data usage and AI training; absent those measures, the likelihood of scraping or unauthorized use is materially increased.