CVE-2026-114386 in AI Harness
Summary
by MITRE • 10/21/2026
Pydantic AI Harness before 0.59.0 fails to enforce UsageLimits.tool_calls_limit on nested tool calls dispatched from run_code in the CodeMode toolset. Users or prompt-injected content can make the sandboxed program invoke tools via run_nested_call() without check_before_tool_call, exceeding operator limits on costly or side-effecting tools.
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Analysis
by VulDB Data Team • 10/21/2026
The vulnerability identified in Pydantic AI Harness prior to version 0.59.0 represents a critical logic flaw within the CodeMode toolset that undermines intended resource governance and security boundaries. Specifically, the system fails to enforce UsageLimits.tool_calls_limit when processing nested tool calls initiated through the run_code function. This architectural oversight allows for an escalation of privileges or resources where external actors can bypass established constraints by leveraging specific API pathways designed for internal code execution. The core issue lies in the disconnect between the initial validation layer and the recursive execution engine, creating a gap that circumvents safety mechanisms intended to protect against excessive resource consumption or unauthorized side effects.
From a technical perspective, the flaw manifests when a sandboxed program invokes tools via run_nested_call(). In a properly secured environment, every tool invocation should trigger a check_before_tool_call mechanism to verify if the operator-defined limits have been reached. However, in this vulnerable version, nested calls dispatched from within the code execution context do not undergo this same rigorous validation process. This omission means that an attacker or malicious prompt injection can chain multiple tool invocations together, effectively multiplying their impact beyond what was originally permitted by the configuration settings. The lack of recursive limit enforcement transforms a controlled sandbox into an unbounded resource consumer.
The operational impact of this vulnerability is significant for any deployment relying on Pydantic AI Harness to manage costly or side-effecting tools such as database modifications, API calls with rate limits, or computationally intensive operations. By exceeding the tool_calls_limit, users can trigger denial-of-service conditions through excessive billing charges or system resource exhaustion. Furthermore, if these nested calls involve actions that modify state, they could lead to data integrity issues or unauthorized access patterns that were not anticipated by the application architects. This is particularly dangerous in multi-tenant environments where one user's exploitation of this flaw could degrade service availability for others sharing the same infrastructure resources.
This vulnerability aligns with CWE-787 Out-of-bounds Write and more specifically CWE-20 Improper Input Validation, as it involves a failure to validate inputs against expected constraints during recursive operations. In terms of offensive security frameworks, it relates to MITRE ATT&CK techniques involving resource hijacking or privilege escalation through logic flaws in application control flow. The ability to bypass limit checks via nested calls is akin to using indirect object references to circumvent access controls, allowing an actor to perform actions that should have been blocked by policy enforcement layers.
To mitigate this risk, organizations must upgrade immediately to Pydantic AI Harness version 0.59.0 or later where the logic for checking tool call limits has been corrected to include nested invocations from run_code. Until upgrading is possible, administrators should implement strict input sanitization on all prompts fed into the CodeMode toolset to prevent prompt injection attacks that could trigger these nested calls. Additionally, deploying network-level rate limiting and monitoring tools for unusual spikes in API usage can help detect exploitation attempts in real-time. It is also advisable to review application logs for patterns of repeated run_nested_call invocations which may indicate active probing or exploitation of this specific logic flaw.