CVE-2026-93595 in ArcadeDB
Summary
by MITRE • 09/18/2026
ArcadeDB before 26.9.1 contains an access control bypass vulnerability in the query_database tool exposed through the AI chat endpoints. The tool executes queries without binding the authenticated principal to DatabaseContext, causing per-type and per-bucket ACL checks to silently no-op and allowing authenticated users to read data they are explicitly denied at the per-type level. Attackers can prompt the AI assistant to execute queries against restricted types or buckets to retrieve sensitive data that would be rejected through normal query endpoints.
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Analysis
by VulDB Data Team • 09/18/2026
The vulnerability identified in ArcadeDB versions prior to 26.9.1 represents a critical access control bypass within the database's artificial intelligence integration layer, specifically affecting the query_database tool exposed via AI chat endpoints. This flaw stems from an implementation error where the execution context for queries initiated through these specific interfaces fails to properly bind the authenticated user principal to the DatabaseContext object. In secure database architectures, every operation must be evaluated against the security policies associated with the identity of the requesting user. By omitting this binding step, the system effectively operates in a state where it lacks awareness of which user is performing the action, thereby breaking the chain of trust required for granular permission enforcement.
The technical consequence of this missing context binding is that access control lists defined at both the per-type and per-bucket levels are rendered ineffective during query execution through the AI chat interface. Normally, when a user submits a request to read or modify data, the database engine checks these ACLs to determine if the specific principal has permission for the targeted schema elements. In this vulnerable scenario, because the context is not bound, these security checks silently no-op, meaning they are skipped entirely rather than returning an explicit denial. This allows authenticated users to bypass restrictions that would otherwise prevent them from accessing sensitive information, effectively granting them elevated privileges equivalent to those of a database administrator or superuser for the duration of the query execution.
From an operational perspective, this vulnerability enables attackers who have valid authentication credentials to retrieve highly restricted data by simply prompting the AI assistant to execute specific queries against protected types or buckets. Unlike traditional SQL injection attacks that rely on malformed input syntax, this exploit leverages legitimate functionality in a way that bypasses security controls entirely. An attacker can craft natural language prompts designed to trigger internal query generation mechanisms within the AI tool, which then executes database commands without applying the necessary permission filters. This results in the exfiltration of sensitive data that is explicitly denied under normal operational conditions through standard API endpoints or direct client connections, leading to significant confidentiality breaches and potential compliance violations depending on the nature of the stored information.
This vulnerability aligns with CWE-269, which describes Improper Privilege Management, specifically where a user gains privileges they are not entitled to due to flawed access control logic. Furthermore, it relates to CWE-862, Missing Authorization, as the system fails to enforce policy decisions regarding resource access based on the authenticated identity. In terms of offensive security frameworks such as MITRE ATT&CK, this behavior is consistent with techniques involving unauthorized access to data through API endpoints or application layer exploitation where authentication exists but authorization checks are circumvented. The attack vector is classified as remote and requires low complexity since it relies on standard AI interaction patterns rather than complex exploit development.
Mitigation strategies must prioritize immediate patching of the ArcadeDB installation to version 26.9.1 or later, which addresses this context binding issue in the query_database tool. For environments where upgrading is not immediately feasible, network-level controls should be implemented to restrict access to AI chat endpoints only from trusted internal networks if possible, reducing the attack surface for external adversaries. Additionally, implementing strict input validation and output filtering on all API endpoints can help detect anomalous query patterns that attempt to access restricted resources. Security monitoring solutions should also be configured to alert on successful queries against protected buckets or types originating from AI service accounts, providing an additional layer of defense through detection rather than prevention until the underlying code defect is resolved.