CVE-2026-19111 in Strands Agents Tools
Summary
by MITRE • 08/07/2026
Insecure direct object reference in the mongodb_memory, elasticsearch_memory, and mem0_memory tools in Amazon Strands Agents Tools before 0.8.3 might allow remote authenticated users to access, modify, or delete memories belonging to other tenants by influencing the LLM to emit tool calls with a forged namespace parameter.
To remediate this issue, users should upgrade to version 0.8.3.
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Analysis
by VulDB Data Team • 08/07/2026
This vulnerability represents a critical insecure direct object reference flaw that affects memory management tools within Amazon Strands Agents Tools ecosystem. The vulnerability specifically impacts mongodb_memory, elasticsearch_memory, and mem0_memory components that handle tenant-specific memory storage and retrieval operations. The security weakness stems from insufficient validation of namespace parameters within tool call execution flows, allowing authenticated attackers to manipulate these parameters and gain unauthorized access to memory resources belonging to other tenants.
The technical implementation flaw occurs when the language model processing system generates tool calls with forged namespace values that bypass normal access control mechanisms. This type of vulnerability maps directly to CWE-639 which defines insecure direct object reference as a condition where an application provides direct access to objects based on user-supplied input without proper authorization checks. The attack vector specifically leverages the LLM's capability to generate tool calls, enabling attackers to manipulate memory operations through crafted inputs that influence the system's namespace handling behavior.
The operational impact of this vulnerability extends beyond simple data exposure to encompass full compromise of tenant isolation within the shared memory infrastructure. An authenticated attacker could potentially read sensitive information stored in other tenants' memory contexts, modify critical operational parameters, or delete essential memory records that might contain confidential data, operational logs, or system configuration details. This represents a severe multi-tenant security breach that undermines fundamental isolation principles and could lead to data leakage, service disruption, or further escalation opportunities within the broader system architecture.
The remediation strategy focuses on upgrading to version 0.8.3 which implements proper namespace validation mechanisms and access control checks for memory operations. This upgrade addresses the root cause by ensuring that tool calls containing namespace parameters undergo strict verification before executing any memory manipulation operations. Security practitioners should also consider implementing additional monitoring for anomalous tool call patterns, establishing proper input sanitization procedures for LLM-generated commands, and validating tenant isolation boundaries through comprehensive penetration testing of the memory management components.
This vulnerability demonstrates how modern AI-powered systems introduce new attack surfaces where natural language processing capabilities can be exploited to bypass traditional security controls. The incident highlights the importance of implementing robust access control mechanisms even within AI-assisted tool execution environments and underscores the need for continuous security assessment of machine learning integrated systems. Organizations should also consider implementing principle of least privilege controls for memory operations and establishing proper audit trails for all memory access activities across tenant boundaries.
The security implications extend to potential lateral movement opportunities where compromised tenants could use this vulnerability to access other tenants' data, potentially leading to broader system compromise. This type of vulnerability is particularly concerning in multi-tenant cloud environments where isolation is paramount for maintaining customer data security and regulatory compliance requirements. Organizations should also evaluate their existing detection capabilities for identifying unauthorized memory access patterns and implement appropriate security controls to prevent similar issues in other AI-powered tools within their infrastructure.
The fix implemented in version 0.8.3 likely includes enhanced parameter validation, improved namespace handling logic, and strengthened authentication checks that ensure only authorized tenants can access their respective memory contexts. This remediation approach aligns with ATT&CK technique T1566 which covers credential harvesting and unauthorized access through exploitation of system vulnerabilities. Security teams should conduct thorough testing to validate the effectiveness of the patch and monitor for any potential side effects in legitimate tool usage patterns while maintaining proper security posture across all memory management operations.