CVE-2026-59318 in Spring AI
Summary
by MITRE • 08/21/2026
In Spring AI's tool calling support, the per-request tool list is advertised to the model as a boundary but is not fully enforced when a tool call is dispatched. Under certain conditions, a tool that was not made available to the current request could be invoked, potentially leading to privilege escalation. Affected versions: Spring AI: 2.0.0 Spring AI: 1.1.0 through 1.1.8 Spring AI: 1.0.0 through 1.0.9
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Analysis
by VulDB Data Team • 08/21/2026
The vulnerability in question resides within the tool calling mechanism of the Spring AI framework, specifically affecting versions 2.0.0 and earlier releases such as 1.1.0 through 1.1.8 and 1.0.0 through 1.0.9. This security flaw stems from a fundamental discrepancy between how tools are advertised to large language models versus how they are actually executed by the application backend. When an integration utilizes Spring AI, developers define a specific set of functions or tools that should be available for the model to invoke during a given request. The framework is responsible for passing this list to the model as a constraint, effectively telling the model which actions it is permitted to take. However, the internal logic governing the dispatching of these tool calls fails to strictly enforce this boundary at runtime.
The technical flaw arises because the validation layer that checks whether an invoked tool belongs to the allowed set for the current request is either missing or insufficiently robust under certain conditions. Consequently, if a large language model decides to invoke a function that was not included in the advertised list for that specific session or context, the Spring AI framework may still proceed with executing that call. This behavior indicates a failure in input validation and access control enforcement at the application layer. The system trusts the output of the external AI service without adequately verifying that the requested action aligns with the predefined security policy for that interaction.
From an operational perspective, this vulnerability can lead to significant privilege escalation scenarios. An attacker who has influence over the context or prompts sent to the model could potentially trick it into requesting access to restricted tools. Since these tools might perform sensitive operations such as accessing internal databases, modifying system configurations, or executing arbitrary code on behalf of the application, their unauthorized invocation poses a severe risk. The impact is particularly acute in multi-tenant environments or applications where different users are granted varying levels of tool access based on their roles. If the framework allows any user to trigger tools intended for administrators or other privileged contexts through prompt injection techniques, it effectively bypasses role-based access controls implemented by the developer.
This issue aligns with CWE Category 284, which covers Improper Access Control, specifically reflecting weaknesses in authorization logic where permissions are not correctly enforced during execution. Furthermore, from a threat modeling perspective using MITRE ATT&CK frameworks, this vulnerability facilitates techniques related to Command and Scripting Interpreter abuse or Privilege Escalation via API manipulation. Attackers can leverage prompt injection strategies to exploit the lack of strict enforcement on tool boundaries, turning legitimate AI interactions into vectors for unauthorized system access.
To mitigate this risk, developers must ensure that they are running patched versions of Spring AI where this boundary enforcement has been corrected. For those unable to upgrade immediately, a critical defensive measure is to implement an additional validation layer within the application code before any tool execution occurs. This involves explicitly checking if the requested function name exists in the whitelist associated with the current request context and rejecting or logging attempts that fall outside these bounds. Additionally, adopting principle of least privilege for AI integrations ensures that even if a bypass occurs, the potential damage is contained by limiting the permissions granted to each specific tool invocation. Regular security audits of prompt engineering practices are also recommended to prevent adversarial inputs from manipulating model behavior toward unauthorized actions.