CVE-2026-84885 in Agent-S
Summary
by MITRE • 09/03/2026
A vulnerability has been found in simular-ai Agent-S 0.3.1/0.3.2. This impacts an unknown function of the file code_agent.py of the component CodeAgent. Such manipulation leads to denial of service. The attack can be launched remotely. The exploit has been disclosed to the public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.
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Analysis
by VulDB Data Team • 09/03/2026
The vulnerability identified within simular-ai Agent-S versions 0.3.1 and 0.3.2 represents a significant reliability risk for systems relying on the CodeAgent component, specifically affecting an undocumented function within the code_agent.py module. This flaw is categorized as a denial of service condition, meaning that while it does not appear to compromise confidentiality or integrity directly through data exfiltration or modification, it severely impacts availability by causing the targeted application or service to crash, hang, or become unresponsive. The nature of this vulnerability suggests an improper handling of specific inputs or states within the code generation logic, leading to resource exhaustion, infinite loops, or unhandled exceptions that terminate the process unexpectedly.
From a technical perspective, such vulnerabilities in autonomous coding agents often stem from edge cases in parsing generated code, executing external commands, or managing memory during complex computational tasks. When an attacker sends specially crafted input designed to trigger these internal errors, they can disrupt the agent's ability to function, effectively halting any automated development workflows that depend on it. This is particularly dangerous in continuous integration and deployment pipelines where such agents are used for code review, generation, or debugging, as a single successful exploit could stall entire build processes or require manual intervention to restore service.
The attack vector for this vulnerability is remote, allowing an adversary who has network access to the affected system to trigger the denial of state without requiring local authentication or physical proximity. This characteristic significantly increases the risk profile, as it expands the potential threat surface beyond trusted internal networks to any internet-facing deployment of simular-ai Agent-S. The fact that a public exploit exists further exacerbates this risk, enabling less sophisticated attackers to leverage known techniques to disrupt services, thereby turning what might have been an obscure bug into a readily available tool for disruption or distraction during more complex attacks.
In terms of industry standards and frameworks, this vulnerability aligns with CWE-400, which covers Uncontrolled Resource Consumption, as the denial of service likely results from excessive resource usage triggered by malicious input. It also maps to MITRE ATT&CK technique T1499, Endpoint Denial of Service, specifically under methods that involve application exhaustion or loop-based disruptions. Understanding these mappings helps in categorizing the risk appropriately within broader security governance frameworks and ensures that mitigation strategies address both the immediate technical flaw and its strategic implications for system availability.
The vendor's lack of response to early disclosure efforts presents a challenge for remediation through official patches, forcing organizations using this software to rely on workarounds or community-driven fixes if available. In such scenarios, it is critical to implement network-level controls that can detect and block anomalous traffic patterns associated with the exploit before they reach the vulnerable component. Additionally, deploying the agent within a sandboxed environment with strict resource limits can mitigate the impact of successful exploitation by containing the crash and preventing cascading failures across other system components.
To address this issue effectively, organizations should prioritize isolating the CodeAgent from untrusted networks until a patch is available or an alternative solution is adopted. Implementing input validation at proxy layers, although potentially difficult for complex agent interactions, can help filter out known malicious payloads associated with the public exploit. Furthermore, maintaining strict monitoring and logging of system resources allows security teams to detect denial of service attempts in real time, enabling rapid incident response actions such as restarting services or isolating affected nodes to maintain overall operational continuity despite the underlying vulnerability.