CVE-2026-91987 in atomic-agents-stack
Summary
by MITRE • 09/15/2026
atomic-agents-stack before 1.1.0 contains a cost-guardrail bypass in the _estimate_batch_cost function that returns zero cost for unknown models not in the pricing table. Attackers can configure deployments with unknown model identifiers to bypass daily cost caps and exceed budget limits in parallel batch operations.
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Analysis
by VulDB Data Team • 09/15/2026
The vulnerability identified within atomic-agents-stack versions prior to 1.1.0 represents a critical logic flaw in the billing estimation subsystem, specifically residing in the _estimate_batch_cost function. This component is responsible for calculating projected expenses associated with large-scale asynchronous processing tasks involving artificial intelligence models. The core technical deficiency lies in the conditional logic used to determine pricing data availability. When the system encounters an AI model identifier that does not have a corresponding entry in the predefined internal pricing table, the function fails to throw an exception or return an error code indicating missing configuration. Instead, it defaults to returning a cost value of zero. This behavior assumes that unknown models are either free or should be ignored for budgeting purposes, which is a fundamental misinterpretation of secure system design principles regarding resource accounting and financial controls.
From the perspective of industry standards, this flaw aligns with CWE-209, which describes the generation of an error message that reveals too much information, although in this specific context, it more accurately reflects CWE-754: Improper Check for Unusual or Exceptional Conditions. The system fails to validate whether a required data lookup was successful before proceeding with financial calculations. Furthermore, this vulnerability can be mapped to the MITRE ATT&CK technique T1083.002, File and Directory Discovery, if we consider the pricing table as a critical configuration resource that is being bypassed through improper handling of missing references. More directly, it relates to CWE-798: Use of Hard-coded Credentials or Resources in terms of relying on incomplete static data structures without fallback mechanisms for unknown inputs. The lack of explicit error handling for unregistered models creates an exploitable gap where the system's assumptions about cost do not match reality.
The operational impact of this vulnerability is significant, particularly for organizations utilizing parallel batch operations to process high volumes of AI requests. Attackers or malicious insiders can exploit this by configuring deployments with model identifiers that are intentionally omitted from the pricing table. By doing so, they effectively bypass daily cost caps and budget limits enforced by the application's guardrails. Since the estimation function returns zero, the system permits these batch operations to proceed without triggering any alerts or halting execution due to excessive projected costs. This allows for unlimited resource consumption at no recorded financial expense, leading to severe cloud infrastructure overruns, unexpected billing spikes from underlying providers like AWS Bedrock or Azure AI Services, and potential denial of service through resource exhaustion if the volume is sufficiently high.
Mitigation strategies must focus on enforcing strict validation policies within the cost estimation logic. The primary fix involves modifying the _estimate_batch_cost function to explicitly check for null or missing pricing data associated with a given model identifier. If such data is absent, the system should raise an exception and abort the batch operation rather than defaulting to zero. Additionally, administrators should implement strict allow-lists for permitted AI models in production environments to prevent the use of unverified identifiers. Regular audits of deployment configurations against current pricing tables are recommended to ensure that all active model versions have corresponding financial entries. Updating atomic-agents-stack to version 1.1.0 or later resolves this issue by implementing proper error handling and validation checks for unknown model costs, thereby restoring the integrity of budget guardrails and preventing unauthorized resource consumption.