A critical use‑after‑free flaw in PyTorch’s mobile interpreter — tracked as CVE‑2024‑31583 — was disclosed in April 2024 and patched in the v2.2.0 release; the bug allowed invalid bytecode indices to reach an unchecked array access in torch/csrc/jit/mobile/interpreter.cpp, producing a deterministic crash or memory corruption that can be used to deny service or, with sufficient skill and favorable memory layout, escalate to more serious memory‑corruption outcomes. (github.com)
PyTorch is a ubiquitous, open‑source machine‑learning framework used across research, edge devices and production services. The vulnerability resides in the mobile JIT interpreter implementation — code that executes compiled TorchScript bytecode on constrained runtimes — and affects all releases prior to v2.2.0. GitHub and multiple vulnerability trackers list the fix as a small, targeted change that adds bounds validation around an instruction operand before indexing into operator arrays, closing a use‑after‑free (UAF) observed by fuzzing. (github.com)
This article walks through the technical root cause, the upstream fix, real‑world impact scenarios, detection and mitigation steps for operations and engineering teams, and the broader supply‑chain lessons for organizations that ship or embed ML runtimes.
Before the patch, the interpreter assumed that the bytecode’s operand X was always a valid index into those arrays. Under some malformed or adversarial bytecode sequences, that assumption could fail and the code would index out of range. That out‑of‑range access could result in dereferencing memory that had been freed or otherwise no longer owned by the process — a classic use‑after‑free. The upstream commit adds an explicit bounds check for inst.X and throws a JITException for invalid instruction indices instead of performing the unsafe access. The change is small but decisive: it converts an unchecked memory read into a controlled exception path. (github.com)
A few subtleties matter for defenders:
Why this is effective: instead of allowing an attacker‑controlled out‑of‑range integer to dereference potentially freed memory, the interpreter now stops execution in a controlled fashion and surfaces an exception — avoiding undefined behavior that leads to UAF.
Operational consequences to consider:
Residual risks remain:
Conclusion
The CVE‑2024‑31583 vulnerability was a high‑impact but well‑contained memory‑safety bug in PyTorch’s mobile interpreter. The remedy — a bounds check and exception path deployed in v2.2.0 — demonstrates how thorough fuzzing and defensive programming stop dangerous UAF conditions. Organizations should treat this disclosure as a prompt to accelerate dependency lifecycle management, harden model‑ingestion boundaries, and ensure that vendor‑embedded copies of core libraries are included in their patch inventories.
Source: MSRC Security Update Guide - Microsoft Security Response Center
Background / Overview
PyTorch is a ubiquitous, open‑source machine‑learning framework used across research, edge devices and production services. The vulnerability resides in the mobile JIT interpreter implementation — code that executes compiled TorchScript bytecode on constrained runtimes — and affects all releases prior to v2.2.0. GitHub and multiple vulnerability trackers list the fix as a small, targeted change that adds bounds validation around an instruction operand before indexing into operator arrays, closing a use‑after‑free (UAF) observed by fuzzing. (github.com)This article walks through the technical root cause, the upstream fix, real‑world impact scenarios, detection and mitigation steps for operations and engineering teams, and the broader supply‑chain lessons for organizations that ship or embed ML runtimes.
What exactly went wrong? Technical root cause explained
The interpreter and a dangerous assumption
The affected file — torch/csrc/jit/mobile/interpreter.cpp — implements the execution loop for mobile TorchScript bytecode. At runtime, bytecode instructions carry operands such as opcodes and indices that are used to look up operator handlers and metadata in per‑model arrays (for example, code.opnames and code.operators_).Before the patch, the interpreter assumed that the bytecode’s operand X was always a valid index into those arrays. Under some malformed or adversarial bytecode sequences, that assumption could fail and the code would index out of range. That out‑of‑range access could result in dereferencing memory that had been freed or otherwise no longer owned by the process — a classic use‑after‑free. The upstream commit adds an explicit bounds check for inst.X and throws a JITException for invalid instruction indices instead of performing the unsafe access. The change is small but decisive: it converts an unchecked memory read into a controlled exception path. (github.com)
Why a bounds check matters
At a high level, a bounds check prevents stale or attacker‑provided indices from being used to dereference arrays that may have been deallocated or repurposed. In practice, UAF can manifest as:- Immediate process crash (denial of service).
- Arbitrary data disclosure (reading freed memory containing sensitive values).
- Control‑flow hijack (if attackers can influence heap layout, overwritten structures or function pointers may be abused to escalate to code execution).
Timeline and upstream remediation
- Vulnerability recorded in public vulnerability databases and GitHub Advisory Database on April 17, 2024. Several downstream trackers mirrored the CVE entry and associated metadata.
- PyTorch maintainers merged a small patch (commit 9c7071b) that adds a bounds check on the instruction operand and throws a JITException for invalid values; the commit message explicitly references a heap UAF discovered by fuzzing and notes the fix prevents a crash that previously reproduced CI‑reported fuzz failures. (github.com)
- The fix was included in the PyTorch 2.2.0 release; advisories and package repositories list 2.2.0 as the remedial version. Vulnerability databases assign the patched version as 2.2.0 and mark all prior releases as affected.
Severity, exploitability and real‑world likelihood
Multiple databases assessed the flaw as High severity. The GitHub Advisory lists a CVSS v3.1 base score of 7.8 (High) and classifies the attack vector as Local with User Interaction required; the same vector and metrics are shown in consolidated advisories.A few subtleties matter for defenders:
- Attack vector: Local / local network — the interpreter must execute attacker‑controlled or malformed bytecode. In many deployments this requires the attacker to supply model files, script bundles, or inputs that reach model loading/execution on a target system. Some embedded or hosted services that accept uploaded models or run third‑party TorchScript could therefore be exposed.
- User interaction: Required — exploitation typically needs the victim to open or load crafted content (for example, a provided model or dataset). This reduces pure remote exploitation likelihood for default server configurations that do not accept untrusted model files, but it does not eliminate risk in environments that process third‑party models.
- Exploitability (EPSS): Public data shows low short‑term exploitation probability (EPSS score historically low for this CVE), reflecting both the need for specialized conditions and the availability of a straightforward upstream fix. That said, EPSS and PoC availability evolve — defenders must assume eventual proof‑of‑concept code could appear.
Who and what is affected?
- Any runtime, service or device that executes the mobile TorchScript interpreter using PyTorch versions older than v2.2.0 is in scope. This includes:
- Mobile/embedded products that rely on the PyTorch mobile runtime.
- Server software that loads or executes TorchScript produced by untrusted sources (for example, model hosting platforms that accept third‑party models).
- Container images and appliance bundles that include older torch wheels or vendor‑embedded PyTorch builds. Several vendor security bulletins call out dependent products that embed torch runtime components.
- Distributions and packages: Linux distribution maintainers and package trackers (Debian, Ubuntu, others) updated their tracking data and pushed fixed packages in affected suites; administrators should check distribution security advisories and upgrade packaged pytorch builds where provided.
The upstream fix — what changed in the code
The committed change adds an explicit bounds check on the instruction operand X before accessing operator name and operator arrays, and throws a high‑level JITException when the index is invalid. The diff is small: four lines added and one removed, but it replaces an unsafe assumption with a deterministic error path. The GitHub commit message explicitly ties the fix to a fuzzing‑found read‑heap‑use‑after‑free and states that the previously reproducible crash no longer occurs after the change. (github.com)Why this is effective: instead of allowing an attacker‑controlled out‑of‑range integer to dereference potentially freed memory, the interpreter now stops execution in a controlled fashion and surfaces an exception — avoiding undefined behavior that leads to UAF.
Practical mitigation and remediation guidance
For most teams the recommended action is straightforward: upgrade to PyTorch v2.2.0 or later as soon as practicable. For complex environments where upgrading requires coordination, use the following guidance.Immediate steps (0–24 hours)
- Inventory: Identify systems that use PyTorch directly or indirectly (containers, packages, wheel files, vendor appliances). Dependency scanning tools and SBOMs speed this up. Use Python package inspection (pip freeze, pip show torch) inside virtualenvs and containers.
- Block ingestion of untrusted models: As a temporary mitigation, prevent the system from loading models or TorchScript files from untrusted sources until patched.
- Apply vendor advisories: If your organization uses third‑party appliances or cloud services, consult vendor support for confirmed status and vendor‑supplied patches. Several vendors (including enterprise software integrators) issued bulletins referencing the PyTorch CVE; follow vendor‑specific mitigation if available.
Short‑term remediation (days)
- Upgrade PyTorch to v2.2.0 or later in development and staging environments; run model‑validation and inference test suites to ensure behavioral compatibility.
- Rebuild and redeploy container images and wheels that embed torch; update CI artifacts to pin the patched version.
- For OS package users, apply distribution updates provided by maintainers (Debian/Ubuntu patch rolls, etc.) and confirm package versions reflect the fixed release.
Long‑term (weeks)
- Add runtime checks and assertive sandboxing around model ingestion:
- Restrict which users or services can upload models.
- Run model‑loading in hardened, isolated processes with minimal privileges.
- Harden supply chain: adopt reproducible builds, pinned dependencies, and automated dependency scanning that flags known CVEs before packaging. Use OSV/GitHub Advisory feeds to keep advisories in your pipeline.
Detection and forensic guidance
- Crash signatures: The interpreter crash that originally triggered upstream fuzzers will typically leave deterministic crash dumps and SIGSEGV traces tied to interpreter.cpp. Search for native crashes in application logs referencing interpreter frames or stack traces containing torch/csrc/jit/mobile/interpreter.cpp. The upstream commit message references a specific fuzzing reproduction; comparing pre‑ and post‑patch crash stacks helps validate remediation. (github.com)
- Memory corruption indicators: Unexpected memory accesses, corrupted model metadata, or sporadic misbehavior when loading certain models are high‑risk indicators and warrant deeper memory forensics.
- Scan your artifacts: Use dependency scanning to find binary wheels or compiled extensions with embedded torch versions older than 2.2.0. Static scanning of container images and packages is efficient for large fleets.
Real‑world attack scenarios and impact analysis
The most likely exploit scenario is an attacker convincing a service or user to load a crafted TorchScript bundle that contains a deliberately malformed instruction index. In hosted model marketplaces or developer sandboxes that accept third‑party models without strict vetting, attackers could use this to crash model runners (availability loss) or target memory corruption to extract secrets from process memory.Operational consequences to consider:
- Denial of service on inference endpoints (sustained if an attacker repeatedly submits the malformed artifact).
- Potential for escalated compromise in environments where the model execution process runs with elevated privileges or has access to secret material in memory.
- Supply‑chain risk where a downstream vendor ships an older embedded PyTorch build in appliances or SDKs; such products may remain vulnerable even if the organization patches direct PyTorch installs. Multiple vendor advisories highlight that third‑party products embedding torch may be affected and must be tracked and patched.
Why this matters beyond the immediate bug
The fragility of ML runtimes
ML runtimes increasingly execute code produced by compilers and model conversion pipelines. The security model must assume that bytecode and model metadata can be adversarial. This CVE is a timely reminder that interpreter invariants must be validated — every index, length and pointer derived from external inputs is a potential attack surface.Supply‑chain and packaging complexities
Organizations often consume PyTorch as a binary wheel or through vendor stacks that bundle older versions. Patching only the application layer while leaving embedded runtimes unchanged produces blind spots. Vulnerability trackers and distribution maintainers flagged the need to update packaged variants; operators must treat embedded libraries as first‑class security assets.The role of fuzzing and targeted CI
Upstream commit messages show fuzzing identified the UAF. This demonstrates the value of automated, corpus‑based testing for interpreters and bytecode engines — publishing fuzz results and triaging them quickly shortens the disclosure window and yields succinct, reviewable fixes. (github.com)Advice for engineers: safe coding and defensive design
- Validate externad types at the earliest boundary. Always treat model bytecode and on‑disk artifacts as untrusted.
- Fail fast and deterministically: prefer exceptions, safe‑reject behavior or sandbox termination over undefined behavior.
- Add runtime assertions and CI hooks to catch out‑of‑range reads in interpreter loops during development.
- Use memory‑sanitizing CI and fuzzing on interpreter code paths; the upstream bug was discovered via fuzzing and fixed with a concise check. (github.com)
How vendors and enterprise teams should respond
- Prioritize visibility: produce an inventory of all components that may include torch, especially embedded or statically linked instances.
- Coordinate with vendors: if you use third‑party appliances or cloud services that embed PyTorch, obtain vendor confirmation on the embedded version and remediation timeline. Vendor security bulletins have already cited CVE‑2024‑31583 against dependent products.
- Update CI/CD: block deployments that include vulnerable torch versions, and add advisory feeds to automated security gating.
Caveats, uncertainties and what we still don’t know
- Public exploit code: As of current public advisories there were no widely circulated PoC exploits demonstrating remote RCE from this CVE; most assessments emphasize denial‑of‑service as the immediate practical impact. That can change — memory corruption bugs sometimes evolve into more powerful exploits once PoCs and memory‑layout techniques (heap grooming) are shared. Defenders should assume worst case and patch accordingly.
- Contextual dependencies: The actual severity for a given environment depends heavily on how the PyTorch interpreter is used, what privileges the process holds, and whether it loads untrusted models. Enterprises must therefore perform a contextual risk assessment rather than rely solely on the CVSS label.
Community and forum signals
WindowsForum and similar community channels have discussed PyTorch security issues and package updates extensively; these community conversations are useful early indicators that downstream packaging or vendor commitments may lag upstream patches, so administrators should watch forum threads as part of their monitoring mix.Checklist — What to do now (executive summary)
- Identify all systems and images that include PyTorch (binary wheels, containers, embedded SDKs).
- If you run torch < 2.2.0, plan an upgrade to 2.2.0+ and rebuild artifacts.
- Block untrusted model ingestion until systems are patched; add input vetting and sandboxing for model loading.
- Apply operating‑system and distribution patches where provided (Debian/Ubuntu package updates).
- Monitor logs for interpreter‑related native crashes and add memory‑corruption alerts to your incident response playbooks. (github.com)
Final analysis: strengths and residual risks
The positive: the upstream response was fast and precise. The fix is small, reviewable, and included in an official release (v2.2.0). The patch addresses the root cause by converting unsafe memory access to a deterministic exception path — classic, correct defensive coding.Residual risks remain:
- Many systems do not or cannot immediately upgrade; vendor‑embedded runtimes and long‑lived appliances may stay vulnerable unless vendors push patches.
- UAFs can be nuanced; while this instance appears to have been mitigated by a bounds check, attackers sometimes chain minor memory issues to achieve more severe outcomes on complex platforms.
- Attackers targeting ML pipelines and supply chains will continue to probe interpreter and conversion code — defenders must bake in continuous hardening, dependency hygiene and model‑input whitelisting.
Conclusion
The CVE‑2024‑31583 vulnerability was a high‑impact but well‑contained memory‑safety bug in PyTorch’s mobile interpreter. The remedy — a bounds check and exception path deployed in v2.2.0 — demonstrates how thorough fuzzing and defensive programming stop dangerous UAF conditions. Organizations should treat this disclosure as a prompt to accelerate dependency lifecycle management, harden model‑ingestion boundaries, and ensure that vendor‑embedded copies of core libraries are included in their patch inventories.
Source: MSRC Security Update Guide - Microsoft Security Response Center
