CVE-2025-9959 in smolagents
Summary
by MITRE • 09/03/2025
Incomplete validation of dunder attributes allows an attacker to escape from the Local Python execution environment sandbox, enforced by smolagents. The attack requires a Prompt Injection in order to trick the agent to create malicious code.
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Analysis
by VulDB Data Team • 09/03/2025
The vulnerability described in CVE-2025-9959 represents a critical security flaw in the smolagents sandboxing mechanism that governs Python execution environments. This issue stems from inadequate validation of dunder attributes, which are special methods in Python that begin and end with double underscores such as _init_, _str_, or _repr_. The weakness allows attackers to exploit the sandbox through prompt injection techniques, effectively bypassing the security controls designed to isolate code execution. The smolagents framework employs a local execution environment to prevent unauthorized access to system resources, but this vulnerability undermines those protective measures by enabling code manipulation through attribute validation gaps.
The technical implementation of this vulnerability involves the manipulation of Python's attribute resolution system where dunder methods are processed differently than regular methods. When an attacker crafts a malicious prompt that includes specially constructed dunder attributes, they can leverage these methods to access underlying system functionality that should remain restricted within the sandboxed environment. This occurs because the validation logic fails to properly sanitize or restrict the creation of these special attributes, allowing them to be interpreted in ways that circumvent the intended security boundaries. The attack vector specifically requires prompt injection as the initial entry point, meaning that the vulnerability cannot be exploited through direct code execution but rather through manipulation of user input that gets processed within the agent's execution context.
The operational impact of this vulnerability extends beyond simple privilege escalation, as it enables attackers to potentially access system resources, execute arbitrary commands, and compromise the integrity of the sandboxed environment. This represents a significant threat to applications that rely on smolagents for secure code execution, particularly those handling untrusted input from users or external sources. The vulnerability's exploitation requires careful crafting of input that can manipulate the agent's attribute handling logic, making it a sophisticated attack that demands both understanding of Python internals and knowledge of the specific sandbox implementation. Organizations using this framework may face data breaches, system compromise, and unauthorized access to sensitive resources if this vulnerability remains unaddressed.
Mitigation strategies for CVE-2025-9959 should focus on implementing comprehensive input validation that specifically addresses dunder attribute creation within the smolagents framework. This includes enforcing strict sanitization of user inputs and implementing attribute validation that prevents the creation of potentially dangerous special methods. Security teams should consider updating to patched versions of smolagents that address the validation gaps, while also implementing monitoring mechanisms to detect suspicious attribute usage patterns. The remediation approach aligns with common security practices outlined in CWE-772 for incomplete input validation and relates to ATT&CK technique T1059.001 for command and scripting interpreter usage. Organizations should also implement principle of least privilege controls and consider additional sandboxing layers to reduce the potential impact of such vulnerabilities. Regular security assessments and code reviews focusing on attribute handling and input validation processes will help prevent similar issues from emerging in other components of the system architecture.