A new denial-of-service vulnerability in Python’s plist parsing library can cause uncontrolled memory allocation and process crashes when parsing malicious Property List (Plist) files, and administrators, developers, and Windows users who run Python-based toolchains should treat this as a high-priority remediation item even though public exploitation evidence remains limited.
Background / Overview
Plist (Property List) files are a common macOS/iOS configuration and metadata format, but they are also processed cross-platform by Python applications that use the standard library’s
plistlib module. The vulnerability tracked as
CVE-2025-13837 stems from plistlib trusting size fields inside a binary Plist and performing reads that preallocate a bytes object of the requested size. An attacker-controlled Plist can therefore request an arbitrarily large allocation (the original report notes ranges up to 2**64), causing the process to allocate huge amounts of memory and either crash or force the host OS to kill the process via OOM mechanisms. The vulnerability is classified as an availability / denial-of-service issue (memory exhaustion) rather than a direct code-execution flaw. The Python core team fixed the root cause by changing plistlib to read large data in bounded chunks rather than attempting a single preallocated read; the preventive patches are already merged into CPython’s codebase and backported to release branches.
What exactly went wrong (technical summary)
The vulnerable behavior
- The plistlib module parsed a size field in a Plist and directly called file.read(n), allowing the runtime to preallocate a bytes object of length n.
- If the Plist declares an excessively large size, the preallocation can exhaust process memory or trigger the system OOM killer. This is a classic resource-exhaustion vector: not a buffer overflow but unbounded allocation.
The code-level fix
- The upstream change introduces a minimum read buffer size constant and replaces direct, single-shot reads with a controlled chunked reading routine that reads at most a fixed chunk per loop iteration.
- The patch adds helper logic (for example, _MIN_READ_BUF_SIZE = 1 << 20) and a loop that loops until the requested bytes are consumed, raising an error if reads return an unexpected size. This bounds the maximum working memory allocated at any one time to a reasonable chunk size proportional to the input file rather than the size field alone.
Affected modules and variants
- The problem is in the plistlib module of CPython. It affects any Python runtime that uses unpatched versions of the standard library where the vulnerable read logic is present.
- The vulnerability was reported and fixed in upstream CPython; the upstream commits, pull request, and associated issue are public and document the change and related backports to release branches.
Scope and impacted populations
Which Python versions are affected
- Public vulnerability trackers and the CPython project metadata indicate that prior-to-patch releases of CPython are affected; maintainers produced targeted backports to supported release branches. The consolidated vulnerability records in public aggregators confirm the CVE and map the fix commits. Administrators should assume any CPython release that predates the merged fix is vulnerable until confirmed otherwise.
How Windows users and admins might be exposed
- Windows systems can be exposed if they run:
- Developer toolchains that process iOS/macOS Plist files (e.g., build servers, CI agents, cross-platform mobile tooling).
- Desktop applications or scripts using Python and plistlib to parse incoming files or configuration (including cross-platform packaging utilities or synchronization tools).
- Server-side ingestion services running on Windows that import or validate client-supplied Plist data.
- Any scenario where untrusted Plist inputs reach a Python process—whether locally invoked on a developer workstation or on a Windows-hosted service—could be used to trigger the condition. Because the vulnerability requires supplying a crafted Plist file, the attack vector is local or file-based, but server-side ingestion pipelines can make it remotely triggerable by letting an attacker upload a crafted file.
Severity and exploitability
- The technical impact is denial of service via out-of-memory (OOM) conditions. Public scoring places the CVSS v4.0 base severity low (CVSS 4.0 = 2.1) in some trackers because it’s not a remote code execution vector by itself; however, CVSS numbers understate operational risk to services that automatically process Plists at scale.
- There is no authoritative public evidence of a working exploit that achieves privilege escalation or remote code execution based solely on this vulnerability at the time of writing; claims that it yields RCE should be treated as unverified unless corroborated by multiple credible sources.
Why Windows administrators should care (practical risk scenarios)
- CI/CD and build servers: A public-facing upload or artifact repository that examines or manipulates Plist content can be induced to process malicious Plists. An attacker who can upload crafted files may cause worker processes to crash or exhaust cluster resources.
- Mobile developers and cross-platform teams: Windows-based build machines that process iOS/macOS bundle metadata (Plist files inside IPAs or bundles) are a natural exposure point.
- Desktop utilities: Tools that parse Plists (for example, migration tools, synchronization agents, or metadata extractors) could be made to crash when fed crafted inputs—causing denials of service for end users.
- Third-party vendor software: Any packaged application that bundles a vulnerable Python runtime (embedded CPython) or statically links Python Stdlib can inherit the vulnerability and requires vendor patches or updates.
Even though the risk of a trivial remote exploit is moderate, the operational risk to Windows-hosted services that do automated processing is real and immediate because it transforms a local parsing flaw into a remotely triggerable denial of service in many architectures.
Confirmed fixes and timeline
- Upstream fix: The CPython project merged a defensive patch that replaces large one-off reads with bounded chunked reads. The code change and commit history are publicly available in the CPython repository; the patch was merged and backported into release branches as part of coordinated security maintenance.
- Database entries: NVD published a record for CVE-2025-13837 that summarizes the issue and references the upstream fixes; OSV, CVE aggregators, and several national CERTs also indexed the CVE and published details.
- Vendor packaging: Linux distributions and packaging ecosystems have mapped the upstream commits for backporting; Windows users should rely on Python.org installers, package managers (winget/chocolatey), and vendor-supplied application updates to receive patched Stdlib bundles.
Practical, prioritized remediation checklist (Windows-focused)
Follow these steps immediately, in order of priority:
- Patch Python installations
- Update CPython to a release that includes the upstream fix or install the official security update from Python.org for your branch. Confirm the distribution’s release notes reference the plistlib fix or the upstream commit IDs if you need absolute verification.
- Apply vendor updates for third-party software
- If an application bundles CPython or ships its own Stdlib, check the vendor’s security advisories and apply updates. Do not assume a vendor’s app uses the OS or system Python.
- Harden file ingestion paths
- Disable automatic preview/rendering of Plist files in any file-upload pipeline.
- Sandbox ingestion workers and run them with least privilege and memory caps.
- Add input validation and limits in code
- If your code calls plistlib on untrusted data, validate file size and/or implement a maximum allowed size before handing the content to plistlib. Consider parsing in a subprocess with resource limits (job object / memory quota) to limit impact.
- Redeploy and restart
- After updating Python or affected applications, restart services and workers to ensure the new Stdlib code is loaded.
- Monitor and escalate
- Watch for spikes in OOM events, repeated process restarts, or unexplained process terminations on hosts that process Plist files.
Developer guidance: secure parsing patterns and mitigations
- Prefer streaming or chunked parsing: avoid APIs that preallocate based on attacker-controlled length fields.
- Enforce explicit maximum file-size policies on any interface that accepts external Plist files (for example, cap to a few megabytes unless business needs dictate otherwise).
- Run untrusted parsing in isolated processes with strict memory quotas or containers; use operating-system enforced memory limits (Job Objects on Windows, cgroups on Linux).
- Add application-level timeouts and memory monitors for long-running parsers.
- If embedding CPython inside a product, ensure the embedded Stdlib is updated and rebuild release images with the patched code.
These mitigations combine programming discipline and runtime barriers to reduce the chance that a single malicious file can cause widespread impact.
Detection, forensics, and incident response
Signals to watch for
- Repeated process crashes or worker restarts in systems that process uploaded documents or mobile artifacts.
- Logging showing Python exceptions related to memory allocation failures or raised InvalidFileException instances from plistlib after the patch pattern (if code raises that exception).
- Elevated OOM-killer events or sudden memory pressure coinciding with uploads or CI job runs.
Hunting
- Correlate upload timestamps with process crashes and inspect the uploaded artifacts for Plist files containing anomalous size declarations.
- Collect process memory dumps and crash artifacts for offline analysis if you suspect exploitation attempts.
- Preserve sample malicious Plists in an offline, sandboxed environment for test reproduction — but do not execute untrusted samples in production.
Critical analysis: strengths, limitations, and risk trade-offs
What went well
- The issue was reported responsibly and the CPython project applied a minimal, low-risk patch that avoids altering public API semantics.
- The upstream patch is concise: limiting per-read buffer sizes by reading large data in controlled chunks is a defensive and typically safe approach that reduces regression risk.
- Multiple ecosystem trackers (NVD, OSV, national CERTs) indexed the CVE quickly, enabling rapid vendor backporting and distribution updates.
Residual risks and caveats
- The fix addresses the immediate resource-exhaustion vector inside plistlib, but the long tail of embedded or statically-linked Python runtimes may remain vulnerable until vendors rebuild and ship fixed artifacts.
- Some third-party software bundles older Stdlib copies; until vendors release updates, organizations must inventory and either patch or mitigate those applications.
- The labeled CVSS severity is low (in several trackers) because the impact is availability-only and requires a file input; however, the operational impact on high-scale ingestion services can be severe and should not be discounted. Treat CVSS numbers as guidance, not the sole prioritization criterion.
Unverifiable or time-sensitive claims to flag
- As of the latest vendor and public tracker updates, there was no verified public exploit that achieves privilege escalation or remote code execution purely through this bug. Any claim to the contrary should be treated cautiously until corroborated by multiple independent security researchers or trusted telemetry feeds.
Step-by-step remediation playbook (ordered actions for teams)
- Inventory and triage (0–2 hours)
- Identify hosts and services that run Python or ship bundled copies of CPython.
- Prioritize public-facing ingestion services and build/CI servers that handle Plists.
- Patch deployments (2–48 hours)
- Apply official CPython updates or vendor application patches in staging/pilot lanes.
- Validate functionality with representative workloads. Reboot services to ensure new code is active.
- Short-term mitigations if patching is delayed (same day)
- Block or quarantine Plist uploads at the gateway and require manual inspection.
- Add request size limits and reject files with suspiciously large size fields.
- Run parsers inside constrained sandboxes or job objects with enforced memory limits.
- Monitoring and validation (1–7 days)
- Increase monitoring of OOM events and process crashes.
- Add detection rules for repeated plistlib InvalidFileException patterns if present.
- Long-term resilience (weeks)
- Enforce dependency hygiene: track and rebuild applications with updated Stdlib.
- Adopt defensive parsing patterns in code reviews and add unit/fuzz tests for file formats.
Conclusion
CVE-2025-13837 is a clear example of how a simple trust assumption—reading a size from a file and preallocating that much memory—can create a practical and exploitable denial-of-service condition across many environments. The CPython project implemented a pragmatic fix that bounds memory usage during Plist parsing, but the real work for organizations is inventorying runtimes, updating deployments, and hardening ingestion paths. Windows administrators and developers who run Python-based toolchains, CI servers, or any system that can receive Plist files must update affected Python runtimes or apply mitigations immediately to avoid service disruption. Cross-check your environment, apply the patch or vendor update, and enforce input limits and sandboxing for untrusted Plist processing.
Source: MSRC
Security Update Guide - Microsoft Security Response Center