CVE-2026-120311 in Network-AI
Summary
by MITRE • 11/02/2026
Network-AI from 5.13.4 before 5.15.5 contains a path traversal vulnerability in FileJobStore that allows attackers to read or delete arbitrary .json files by supplying traversal sequences in the job id. Attackers controlling the id passed to FileJobStore.get(), FileJobStore.delete(), or JobQueue.getJob() can disclose credentials and grant tokens or delete configuration files to weaken AuthGuardian authorization.
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Analysis
by VulDB Data Team • 11/02/2026
The vulnerability identified within Network-AI versions prior to 5.15.5, specifically affecting the range from 5.13.4 through earlier releases, represents a critical path traversal flaw located in the FileJobStore component. This security defect arises due to insufficient validation of user-supplied input when handling job identifiers passed into core storage and retrieval functions such as get(), delete(), and JobQueue.getJob(). The underlying technical mechanism involves the application's failure to properly sanitize or canonicalize file paths constructed from these identifiers, allowing an attacker to inject directory traversal sequences like dot-dot-slash. By manipulating this parameter, a remote actor can bypass intended access controls that restrict operations to specific directories within the application's working environment. This lack of input validation is a classic example of CWE-22 Improper Limitation of a Pathname to a Restricted Directory, where the software does not adequately neutralize special elements in file paths that could resolve to unintended locations outside the restricted directory tree.
The operational impact of this vulnerability is severe due to its direct effect on data integrity and confidentiality within the application's job management system. An attacker who can control or influence the job ID parameter used by these functions gains the ability to read arbitrary JSON files stored in accessible directories, which often contain sensitive configuration data, authentication credentials, API keys, and session tokens. The disclosure of such information compromises the confidentiality of the entire network infrastructure managed by Network-AI. Furthermore, the delete functionality allows for the destruction of critical .json configuration files. This capability enables a denial-of-service condition or, more insidiously, facilitates privilege escalation attacks by removing security configurations that enforce authorization policies. Specifically, deleting these files can weaken AuthGuardian authorization mechanisms, potentially allowing unauthorized users to bypass authentication checks and gain elevated privileges within the system.
From an offensive perspective, this vulnerability aligns with ATT&CK technique T1083 File and Directory Discovery, as it allows for enumeration of sensitive files through path manipulation. Additionally, if exploited successfully to delete configuration files or alter application state, it falls under T1562 Impair Defenses, specifically by disabling security software configurations such as AuthGuardian rules. The ability to read credential stores directly maps to T1078 Valid Accounts and potentially T1552 Unsecured Credentials if the JSON files contain plaintext secrets. For defenders and system administrators, immediate mitigation involves upgrading Network-AI to version 5.15.5 or later where this path traversal issue has been remediated through improved input validation and strict file path canonicalization checks before any filesystem operations are executed. In environments where an upgrade is not immediately feasible, implementing a web application firewall rule set that blocks common directory traversal patterns in job ID parameters can provide temporary protection against exploitation attempts while ensuring that the FileJobStore component operates with restricted permissions to limit the blast radius of potential file access violations.