CVE-2026-59294 in Spring AI
Summary
by MITRE • 08/27/2026
ResourceCacheService.getCacheName() builds the on-disk filename by appending the URI fragment verbatim, without stripping path separators or .. sequences, and passes the result to new File(resourceParentFolder, newFileName) before writing the downloaded bytes there. Spring AI 2.0.0 Spring AI 1.1.0 - 1.1.8 Spring AI 1.0.9 and earlier
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Analysis
by VulDB Data Team • 08/27/2026
The vulnerability identified in Spring AI versions ranging from 1.0.9 through 2.0.0 involves a critical path traversal flaw within the ResourceCacheService component, specifically located in the getCacheName method. This function is responsible for determining the file system location where downloaded resources are stored locally to optimize performance by avoiding redundant network requests. The core technical deficiency lies in the handling of Uniform Resource Identifier fragments provided during resource resolution operations. Instead of sanitizing or normalizing these URI components, the service appends them directly to a parent directory path without performing any validation checks for malicious sequences such as double dots representing parent directory navigation or forward slashes indicating subdirectory traversal. This lack of input validation allows an attacker who can influence the source URI used in AI model resource loading operations to dictate arbitrary file system paths relative to the application's cache root directory.
From a technical perspective, this flaw constitutes a classic Path Traversal vulnerability, formally categorized under CWE-22: Improper Limitation of a Filename for Directory Traversal. The mechanism is straightforward yet dangerous because Java File constructors do not inherently prevent path traversal when provided with absolute or relative paths containing escape sequences. By passing the unsanitized URI fragment directly to new File(resourceParentFolder, newFileName), the application effectively trusts user-supplied input without verifying that it remains within the intended sandboxed directory structure. This behavior violates fundamental security principles regarding file system access control and boundary enforcement, allowing external actors to break out of the designated cache folder into higher-level directories or even absolute paths depending on the operating environment's permissions.
The operational impact of this vulnerability is severe due to its potential for arbitrary file write operations within the context of the running application process. An attacker could overwrite critical configuration files, inject malicious scripts into executable locations, or corrupt existing cached data by writing payloads outside the expected directory boundaries. In cloud-native environments where Spring AI applications are commonly deployed, such a flaw can lead to container escape scenarios if combined with other vulnerabilities, as well as persistent compromise through backdoor installation in accessible directories like /tmp or home directories of service accounts. The risk is further amplified because resource loading often occurs during model initialization or dynamic retrieval phases which may be triggered by user inputs processed via AI prompts or external data sources integrated into the application workflow.
This vulnerability aligns with MITRE ATT&CK technique T1048: Exfiltration Over Alternative Protocol, particularly in scenarios where attackers use the file write capability to stage exfiltrated data in hidden locations for later retrieval using non-standard protocols. It also relates to T1562: Impair Defenses through modification of system configurations if critical files are overwritten. The absence of proper input validation represents a significant deviation from secure coding practices recommended by OWASP, specifically failing to enforce strict allow-listing or canonicalization of file paths before disk operations. Developers must recognize that relying on framework defaults for resource caching without explicit security hardening exposes the application to remote code execution prerequisites and data integrity violations.
Mitigation strategies require immediate patching to Spring AI versions newer than 2.0.0 where this issue has been addressed through enhanced input sanitization routines. For applications unable to upgrade immediately, implementing a custom ResourceCacheService that overrides getCacheName with robust path validation logic is necessary. This includes normalizing paths using canonical representation methods and explicitly rejecting any resulting path that does not start with the expected cache directory prefix after normalization. Additionally, applying principle of least privilege by running application processes under restricted user accounts limits the damage potential even if traversal succeeds. Regular security audits focusing on file system interactions and integration testing against path manipulation payloads should be conducted to ensure no similar vulnerabilities exist in other parts of the codebase handling external resource references.