CVE-2026-62675 in Omnigent
Summary
by MITRE • 08/21/2026
Omnigent is an open-source AI agent framework and meta-harness for orchestrating coding agents. Prior to 0.3.0, multipart POST /v1/sessions accepts an authenticated user's agent bundle and omnigent/server/bundles.py validate_agent_bundle does not reject a tools..callable dotted Python path. omnigent/runner/tool_dispatch.py _resolve_spec_callable imports the specified module and _execute_spec_callable_tool invokes the resolved function, allowing a bundle to select subprocess.check_output and execute a local command with the runner process permissions. This can expose runner files, environment variables, credentials, workspace data, internal services, and availability without administrator access. This issue is fixed in version 0.3.0.
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Analysis
by VulDB Data Team • 08/21/2026
The vulnerability identified within the Omnigent AI agent framework prior to version 0.3.0 represents a critical server-side request forgery and arbitrary code execution flaw rooted in insufficient input validation of user-supplied configuration data. Omnigent serves as an open-source meta-harness designed for orchestrating coding agents, allowing users to submit agent bundles that define specific behaviors and tool integrations. The core technical failure occurs within the multipart POST endpoint /v1/sessions, where authenticated users can upload these bundles. The function validate_agent_bundle in omnigent/server/bundles.py is responsible for sanitizing incoming bundle configurations but fails to reject or properly restrict dotted Python paths specified under tools..callable fields. This oversight allows an attacker to specify arbitrary module and attribute names that the server will subsequently attempt to import and execute without adequate security checks, effectively bypassing intended sandboxing mechanisms.
The operational impact of this flaw is severe due to the direct execution context in which the resolved functions are invoked. When a malicious bundle specifies a callable path such as subprocess.check_output, the system proceeds through _resolve_spec_callable in omnigent/runner/tool_dispatch.py to import the specified module and resolve the function reference. Subsequently, _execute_spec_callable_tool invokes this function with arguments provided by the user or derived from the agent's logic. Because these operations are performed within the runner process permissions, an attacker can execute arbitrary system commands on the host machine hosting the Omnigent service. This capability transforms a seemingly benign configuration submission into a full remote code execution vector, granting the attacker control equivalent to that of the application runtime account.
The consequences of this unauthorized command execution extend beyond simple shell access. An adversary leveraging this vulnerability can exfiltrate sensitive runner files and workspace data stored on the server filesystem. Furthermore, because the process runs with specific environmental contexts, attackers can read environment variables which often contain database credentials, API keys, cloud provider secrets, or internal service endpoints. This exposure compromises not only the integrity of the Omnigent platform but also potentially impacts downstream systems that rely on these leaked credentials for authentication and authorization. Additionally, the ability to execute arbitrary commands allows for denial-of-service conditions by consuming system resources or disrupting critical services, thereby affecting availability without requiring any form of administrator access privileges.
From a classification perspective, this vulnerability aligns with CWE-94 Improper Control of Generation of Code (Code Injection) as it involves the construction and execution of code based on user input. It also maps to ATT&CK technique T1059 Command and Scripting Interpreter, specifically sub-techniques involving system-level commands like sh or cmd depending on the operating environment, illustrating how attackers can leverage legitimate application features for malicious purposes. The lack of strict allow-listing for callable modules is a classic example of insecure deserialization patterns where trust in input structure is misplaced.
To mitigate this risk, organizations running versions prior to 0.3.0 must upgrade immediately to the patched version which implements stricter validation rules for tool callables. In environments where upgrading is not instantly feasible, network-level controls should be applied to restrict access to the /v1/sessions endpoint exclusively to trusted internal networks or authenticated administrative users with multi-factor authentication enabled. Additionally, running the Omnigent runner process under a restricted user account with minimal file system and network permissions can limit the blast radius of any successful exploitation attempt. Implementing strict allow-lists for permitted Python modules in custom deployments before patching is also recommended as an interim defense-in-depth measure to prevent arbitrary imports from being resolved by the framework.