Cyber Resilience

CVE-2026-26006

Agpt Autogpt Platform 0.4.0 – 0.6.32

Public PoC
Published
10 February 2026
Modified
17 February 2026
Patch / advisory
CVSS Score v3.1 6.5
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
EPSS Score 0.0047 39th percentile
Risk Priority 50 floored blend · peak EPSS

Summary

CVE-2026-26006 is a medium-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability in Agpt Autogpt Platform. Its CVSS base score is 6.5 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 39th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Supply Chain and Deployment risk domain.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.

EU & UK References

Vulnerability Data

AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. The autogpt before 0.6.32 is vulnerable to Regular Expression Denial of Service due to the use of regex at Code…

more

Extraction Block. The two Regex are used containing the corresponding dangerous patterns \s+[\s\S]*? and \s+(.*?). They share a common characteristic — the combination of two adjacent quantifiers that can match the same space character (\s). As a result, an attacker can supply a long sequence of space characters to trigger excessive regex backtracking, potentially leading to a Denial of Service (DoS). This vulnerability is fixed in 0.6.32.

CWE(s)

AI Security AnalysisAI

AI Category
AI Agent Protocols and Integrations
Risk Domain
Supply Chain and Deployment
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: artificial intelligence, autogpt

Related Threats

MITRE ATT&CK Enterprise Techniques

T1499 Endpoint Denial of Service Impact
Adversaries may perform Endpoint Denial of Service (DoS) attacks to degrade or block the availability of services to users.
T1499.003 Application Exhaustion Flood Impact
Adversaries may target resource intensive features of applications to cause a denial of service (DoS), denying availability to those applications.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2025-32393Same product: Agpt Autogpt Platform
CVE-2025-32425Same product: Agpt Autogpt Platform
CVE-2025-62616Same product: Agpt Autogpt Platform
CVE-2025-31491Same product: Agpt Autogpt Platform
CVE-2025-31490Same product: Agpt Autogpt Platform
CVE-2026-26020Same product: Agpt Autogpt Platform
CVE-2025-0454Same product: Agpt Autogpt Platform
CVE-2026-22038Same product: Agpt Autogpt Platform
CVE-2025-1040Same product: Agpt Autogpt Platform
CVE-2026-24780Same product: Agpt Autogpt Platform

Affected Assets

agpt
autogpt platform
0.4.0 — 0.6.32

Mitigating Controls

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover inefficient regex patterns via performance or static analysis.

Development standards and tools can require safe regex construction and forbid known exponential patterns.

Denial-of-service protections limit resource exhaustion caused by expensive regex evaluation.

Input validation can constrain data that would otherwise trigger worst-case regex complexity.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly prevent inefficient regex via reviews, static analysis, and safe libraries.

ID.RA-01 partial match
prevents

Vulnerability identification processes can discover ReDoS issues in existing code but do not stop their introduction.

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

finds

Security testing can detect and reject regex patterns with exponential worst-case complexity.

prevents

Secure development lifecycle mandates review of algorithmic efficiency, directly addressing ReDoS-prone regex.

prevents

Application security requirements can specify input-validation rules that limit regex complexity.

prevents

Secure architecture principles encourage avoidance of computationally expensive constructs such as catastrophic backtracking.

prevents

Secure coding standards explicitly prohibit or limit the use of inefficient regular expressions.

References