CVE-2024-28219

What it actually does

Pillow versions before 10.3.0 contain a memory-safety flaw in the C extension component _imagingcms.c. The vulnerable code uses strcpy for a string copy without enforcing the destination buffer length, allowing an overlong value processed by Pillow’s color-management functionality to overwrite adjacent memory. Successful exploitation can corrupt process memory and may lead to interpreter crashes, denial of service, or, under favorable memory-layout conditions, arbitrary code execution in the context of the process using Pillow. The CVSS vector indicates local attack requirements, high attack complexity, low privileges, and user interaction, so exploitation is not expected to be reliably wormable but can be serious where untrusted image/profile processing occurs.

Why it matters to the business

Applications, automation jobs, desktop tools, and services that use vulnerable Pillow versions to process images may be exposed to crashes or potential compromise if they handle attacker-supplied image or color-profile data. Business consequences can include disruption of image-processing pipelines, failed uploads or conversions, worker process instability, and possible unauthorized access to data or systems reachable by the affected process. The vulnerability is not in CISA KEV, is not currently associated with ransomware use, and has a low EPSS score, but Pillow is widely used across Python ecosystems, so stale transitive dependencies can create broad residual exposure.

How an attack unfolds

An attacker first identifies software that uses a vulnerable Pillow release and exercises Pillow’s color-management path, such as code that opens, converts, transforms, or validates images with embedded or associated color profiles. The attacker then supplies a specially crafted local file or otherwise induces a user or application workflow to process the malicious image/profile data. When the vulnerable _imagingcms C extension copies an overly long string with strcpy, memory adjacent to the target buffer can be overwritten. In the simplest outcome, the Python process crashes, producing a denial of service. A more sophisticated attacker would attempt to shape memory layout and payload contents to gain control of execution, but the high attack complexity and lack of public exploit maturity make reliable exploitation less likely than crash-based impact.

How to fix it

Upgrade Pillow to version 10.3.0 or later. For source-based consumers tracking the Pillow Git repository, ensure the code includes commit 5c89d88eee199ba53f64581ea39b6a1bc52feb1a or a later commit containing the fix. Rebuild and redeploy all Python virtual environments, containers, serverless packages, desktop bundles, and downstream images that vendor or pin Pillow. Review dependency lockfiles such as requirements.txt, poetry.lock, Pipfile.lock, conda environment files, and container build manifests for indirect Pillow pins. Where immediate upgrade is not possible, reduce exposure by avoiding processing of untrusted images or ICC profiles, disabling nonessential color-management operations, running image processing in a sandboxed low-privilege worker, applying resource limits, and isolating conversion workers from sensitive systems and credentials.

How to confirm the fix worked

Confirm that all runtime environments resolve Pillow to 10.3.0 or later using package inventory, SBOM, lockfile review, or runtime checks such as `python -c "import PIL; print(PIL.__version__)"`. For source builds, verify that the deployed source includes commit 5c89d88eee199ba53f64581ea39b6a1bc52feb1a or later and that native extension artifacts were rebuilt rather than reused from an older build cache. Validate container and artifact layers because older Pillow wheels may remain in base images or vendored application directories. Exercise representative image-processing workflows after upgrade to confirm that ImageCms and ICC-profile handling still function. Exposure remains if any active application, worker, notebook image, CLI tool, or packaged binary continues to load Pillow before 10.3.0.

Typical attack path

1. Find an application, script, worker, or desktop workflow using Pillow before 10.3.0.
2. Determine whether the workflow invokes Pillow color-management functionality, such as ICC profile handling or ImageCms operations.
3. Create or obtain an image/profile input containing an overlong value that reaches the vulnerable strcpy call in _imagingcms.c.
4. Deliver the crafted file through a local workflow, upload pipeline, shared storage location, email attachment, or other mechanism that causes a user or process to open or transform it.
5. Trigger Pillow processing of the malicious image/profile data.
6. Cause memory corruption in the Python process, resulting in a crash or, in a more advanced scenario, execution of attacker-controlled code in the affected process context.

How to detect it

There are no public, unique exploitation indicators for this vulnerability, and exploitation attempts may look like ordinary image-processing failures unless they cause crashes. Review application logs, worker supervisor logs, system journals, crash reports, and core-dump metadata for Python processes terminating while processing images or invoking Pillow ImageCms functionality. Useful log sources include web upload logs, media conversion job logs, Celery/RQ/worker logs, container stdout/stderr, systemd journal, kernel crash logs, endpoint telemetry, EDR process-crash events, and core dump services such as apport, ABRT, systemd-coredump, or Kubernetes crash-loop telemetry. Look for segmentation faults, access violations, illegal memory writes, repeated worker restarts, or abnormal exits referencing PIL._imagingcms, _imagingcms, ImageCms, or lcms during processing of recently supplied image files. Correlate crash timestamps with file upload events, attachment ingestion, batch import jobs, or new files appearing in watched directories. Preserve suspicious image/profile inputs and core dumps for forensic analysis, but handle them in an isolated environment. Because successful code execution would run inside the hosting Python process, also investigate unexpected child processes, outbound network connections, credential access, or file modifications spawned by image-processing workers around the time of a crash.

• PIL._imagingcms
• _imagingcms
• ImageCms
• PIL.ImageCms
• Segmentation fault
• segfault at
• core dumped
• systemd-coredump
• Fatal Python error
• access violation
• Illegal instruction
• worker exited unexpectedly
• CrashLoopBackOff

What your security team should do

• Inventory all Python applications, containers, notebooks, automation jobs, and packaged tools that include Pillow, including transitive dependencies.
• Prioritize upgrade for systems that process user-supplied images, email attachments, uploaded media, documents, or external datasets.
• Upgrade to Pillow 10.3.0 or later, or ensure source builds include commit 5c89d88eee199ba53f64581ea39b6a1bc52feb1a.
• Rebuild and redeploy affected containers and virtual environments to remove stale native extension artifacts.
• Add dependency-policy checks to block Pillow versions earlier than 10.3.0 in CI/CD and software composition analysis tooling.
• Run image-processing services with least privilege, sandboxing, resource limits, and network egress restrictions to reduce impact from native-code memory corruption.
• Review logs for unexplained Python worker crashes, segmentation faults, or core dumps involving PIL._imagingcms or ImageCms before the upgrade window.