Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-40192 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability in Python Pillow. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Network Denial of Service (T1498); ranked at the 49th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Computer Vision; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to AC-10 (Concurrent Session Control) and SC-5 (Denial-of-service Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-40192 affects Pillow, a widely used Python imaging library, specifically versions 10.3.0 through 12.1.1. The vulnerability stems from a lack of limits on the amount of GZIP-compressed data read during the decoding of FITS images, enabling decompression bomb attacks. This flaw, classified under CWE-400 (Uncontrolled Resource Consumption) and CWE-770 (Allocation of Resources Without Limits or Throttling), allows a specially crafted FITS file to trigger unbounded memory consumption, resulting in denial of service through out-of-memory crashes or severe performance degradation. The issue carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).
Attackers can exploit this vulnerability remotely without authentication or user interaction by delivering a malicious FITS file to a vulnerable Pillow installation. Any application or service processing untrusted FITS images—such as astronomical data pipelines, image viewers, or web services handling scientific imagery—is at risk. Successful exploitation leads to resource exhaustion, potentially crashing the affected process or host, though no confidentiality or integrity impacts are possible.
Pillow advisories and release notes recommend upgrading to version 12.2.0 or later, where the commit (3cb854e8b2bab43f40e342e665f9340d861aa628) and pull request (#9521) implement limits on GZIP decompression for FITS files. As a temporary workaround, users unable to upgrade immediately should restrict image processing to exclude FITS format when handling untrusted inputs. Details are available in the GitHub security advisory (GHSA-whj4-6x5x-4v2j) and Pillow 12.2.0 release notes.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-23130
Vulnerability Data
Pillow is a Python imaging library. Versions 10.3.0 through 12.1.1 did not limit the amount of GZIP-compressed data read when decoding a FITS image, making them vulnerable to decompression bomb attacks. A specially crafted FITS file could cause unbounded memory…
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consumption, leading to denial of service (OOM crash or severe performance degradation). If users are unable to immediately upgrade, they should only open specific image formats, excluding FITS, as a workaround.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Computer Vision
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: pillow
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 5 hardening rules · 3 OS baselines
V15.4.4
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces a hard limit on concurrent sessions, structurally preventing unbounded resource allocation.
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Input validation can reject or limit decompression of data whose expansion ratio exceeds safe thresholds.
Imposes a limit on consecutive invalid attempts, preventing one specific class of unbounded resource consumption.
Process isolation confines resource consumption to separate domains, reducing blast radius without stopping the root flaw.
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.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Secure-development practices include input-validation and resource-limit checks that prevent improper handling of compressed data.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
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.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
Security testing can uncover decompression-bomb vulnerabilities before release.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (2 rules)
- V-248552 OL 8 must be configured so that all network connections associated with SSH traffic terminate after becoming unresponsive. prevents CWE-770
- V-248553 OL 8 must be configured so that all network connections associated with SSH traffic are terminated after 10 minutes of becoming unresponsive. prevents CWE-770
Oracle Linux 9 (2 rules)
- V-271710 OL 9 must be configured so that all network connections associated with SSH traffic are terminated after 10 minutes of becoming unresponsive. prevents CWE-770
- V-271709 OL 9 must be configured so that all network connections associated with SSH traffic terminate after becoming unresponsive. prevents CWE-770
RHEL 8 (1 rule)
- V-230244 RHEL 8 must be configured so that all network connections associated with SSH traffic terminate after becoming unresponsive. prevents CWE-770