CVE-2026-18394 in Strands Agents Tools
Summary
by MITRE • 07/31/2026
Incorrect authorization in the http_request tool in Strands Agents Tools before 0.8.2 might allow remote attackers to obtain credentials configured via HTTP_REQUEST_TOKEN_CONFIG by influencing the LLM to route requests through actor-controlled proxy infrastructure.
To remediate this issue, users should upgrade to version 0.8.2.
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Analysis
by VulDB Data Team • 07/31/2026
This vulnerability represents a critical authorization flaw in the http_request tool component of Strands Agents Tools, specifically affecting versions prior to 082. The security weakness stems from improper validation of request routing decisions made by large language models within the agent framework, creating a pathway for remote attackers to access sensitive credential configurations. The vulnerability manifests when an attacker can influence the LLM to route HTTP requests through proxy infrastructure they control, effectively bypassing intended authorization mechanisms that should prevent unauthorized access to HTTP_REQUEST_TOKEN_CONFIG variables containing authentication credentials.
The technical implementation flaw lies in the insufficient input validation and routing control within the agent's HTTP request handling system. When the LLM generates requests, it operates without proper checks to ensure that all network traffic adheres to predefined security boundaries. This allows attackers to manipulate the agent's decision-making process through carefully crafted prompts or inputs that direct the tool to use attacker-controlled proxy servers. The vulnerability directly relates to CWE-285 which addresses improper authorization in software systems, specifically when access controls fail to properly validate request routing decisions.
From an operational impact perspective, this vulnerability enables credential theft at scale, as attackers can systematically harvest authentication tokens and other sensitive configuration data without proper authorization. The attack vector leverages the trust relationship between the LLM and network infrastructure, making detection particularly challenging since legitimate-looking requests are being routed through malicious proxies. This creates a sophisticated attack scenario where the attacker can maintain persistent access to credential stores while appearing to operate within normal system parameters.
The remediation strategy focuses on upgrading to version 0.8.2 which includes enhanced authorization controls and improved request routing validation. This update implements mandatory checks that prevent LLM-generated requests from being directed through untrusted proxy infrastructure, thereby closing the credential exposure path. Security practitioners should also implement additional monitoring for unusual proxy usage patterns and consider implementing network-level controls to restrict outbound HTTP traffic from agent systems to prevent similar vulnerabilities in other components of the agent framework.
This vulnerability demonstrates the complex security challenges inherent in AI agent systems where machine learning models interact with traditional security controls. The attack surface expands significantly when LLMs are allowed to make network routing decisions without proper authorization boundaries, creating opportunities for attackers to exploit the trust relationship between the model and underlying infrastructure. Organizations deploying similar agent-based systems should conduct comprehensive security assessments of their LLM integration points to identify potential authorization bypasses that could lead to credential exposure and privilege escalation attacks.
The flaw aligns with ATT&CK technique T1566 which covers social engineering through AI tools, as attackers can manipulate LLM behavior to achieve unauthorized access. Additionally, the vulnerability represents a classic case of insufficient input validation that allows for proxy-based credential theft, making it particularly dangerous in environments where agents have elevated network privileges. Proper patch management and security configuration reviews are essential to prevent exploitation of this type of authorization bypass vulnerability in similar software ecosystems.
Implementation of this upgrade addresses the core authorization failure by introducing mandatory validation of all HTTP request destinations, ensuring that only pre-approved endpoints can receive traffic from the agent tool. The fix specifically targets the LLM routing decision-making process to prevent unauthorized proxy usage while maintaining legitimate functionality for authorized network operations. Organizations should also consider implementing principle of least privilege controls for their agent systems and establishing robust monitoring for any unauthorized network access patterns that might indicate exploitation attempts.