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CRITICAL severity

CVE-2026-22778 vllm

CRITICALFix: vllm-project/vllm#31987

CVE-2026-22778 is a critical-severity (CVSS 9.8) CWE-532 vulnerability in vllm. A fix is available for vllm — see the affected versions and patch details below.

vLLM leaks a heap address when PIL throws an error

Also known asGHSA-4r2x-xpjr-7cvvPYSEC-2026-565
Published
Feb 2, 2026
Updated
Aug 12, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Sep 21, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
  • A successful exploit gives an attacker total control of the affected component, not partial access.
  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for CVE-2026-22778.

EPSS Exploitation Probability

via FIRST.org ↗
3.8%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs90th percentile — riskier than 90% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

CVE-2026-22778 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 377,636 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

1 pkg affected
🐍vllm

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

A chain of vulnerabilities in vLLM allow Remote Code Execution (RCE):

  1. Info Leak - PIL error messages expose memory addresses, bypassing ASLR
  2. Heap Overflow - JPEG2000 decoder in OpenCV/FFmpeg has a heap overflow that lets us hijack code execution

Result: Send a malicious video URL to vLLM Completions or Invocations for a video model -> Execute arbitrary commands on the server

Completely default vLLM instance directly from pip, or docker, does not have authentication so "None" privileges are required, but even with non-default api-key enabled configuration this exploit is feasible through invocations route that allows payload to execute pre-auth.

Example heap target is provided, other heap targets can be exploited as well to achieve rce. Leak allows for simple ASLR bypass. Leak + heap overflow achieves RCE on versions prior to 0.14.1.

Deployments not serving a video model are not affected.


1. Vulnerability Overview

1.1 The Bug: JPEG2000 cdef Box Heap Overflow

The JPEG2000 decoder used by OpenCV (cv2) honors a cdef box that can remap color channels. When Y (luma) is mapped into the U (chroma) plane buffer, the decoder writes a large Y plane into the smaller U buffer, causing a heap overflow.

Root Cause

  • cdef allows channel remapping (e.g., Y→U, U→Y).
  • Y plane size: W×H; U plane size: (W/2)×(H/2).
  • Overflow size = W×H - (W/2×H/2) = 0.75 × W × H bytes.

Example (150×64)

  • Y plane: 150×64 = 9,600 bytes
  • U plane: 75×32 = 2,400 bytes
  • Overflow: 7,200 bytes past the U buffer

1.2 Malicious cdef Box

Offset  Size  Field           Value
0       4     Box Length      0x00000016 (22 bytes)
4       4     Box Type        'cdef'
8       2     N (channels)    0x0003
10      2     Channel 0 Cn    0x0000 (Y channel)
12      2     Channel 0 Typ   0x0000 (color)
14      2     Channel 0 Asoc  0x0002 (→ maps Y into U plane)
16      2     Channel 1 Cn    0x0001 (U channel)
18      2     Channel 1 Typ   0x0000 (color)
20      2     Channel 1 Asoc  0x0001 (→ maps U into Y plane)
22      2     Channel 2 Cn    0x0002 (V channel)
24      2     Channel 2 Typ   0x0000 (color)
26      2     Channel 2 Asoc  0x0003 (→ maps V plane)

Key control: Asoc=2 for channel 0 forces Y data into the U buffer, triggering the overflow.


Vulnerable Code Chain

1) Entry: vLLM accepts a remote video_url and downloads raw bytes

vLLM’s OpenAI-compatible API supports a video_url content part:

class VideoURL(TypedDict, total=False):
    url: Required[str]

class ChatCompletionContentPartVideoParam(TypedDict, total=False):
    video_url: Required[VideoURL]
    type: Required[Literal["video_url"]]

Source: src/vllm/entrypoints/chat_utils.py.

When the URL is HTTP(S), vLLM downloads it as raw bytes and passes the bytes into the modality loader:

if url_spec.scheme.startswith("http"):
    data = connection.get_bytes(url, timeout=fetch_timeout, allow_redirects=...)
    return media_io.load_bytes(data)

Source: src/vllm/multimodal/utils.py (MediaConnector.load_from_url).


2) Decode: vLLM uses OpenCV (cv2) VideoCapture on an in-memory byte stream

The default video backend is OpenCV, and it constructs cv2.VideoCapture over a BytesIO buffer containing the downloaded bytes:

backend = cls().get_cv2_video_api()
cap = cv2.VideoCapture(BytesIO(data), backend, [])
if not cap.isOpened():
    raise ValueError("Could not open video stream")

Source: src/vllm/multimodal/video.py (OpenCVVideoBackend.load_bytes).

The backend is selected from OpenCV’s stream-buffered backends registry:

import cv2.videoio_registry as vr
for backend in vr.getStreamBufferedBackends():
    if vr.hasBackend(backend) and ...:
        api_pref = backend
        break
return api_pref

Source: src/vllm/multimodal/video.py (OpenCVVideoBackend.get_cv2_video_api).

Implication: vLLM is delegating container parsing + codec decode to OpenCV’s Video I/O stack (which, in typical builds, is backed by FFmpeg for MOV/MP4 and codecs like JPEG2000).


3) The actual overflow: Y (full-res) written into U (quarter-res)

When the decoder honors the remap and writes Y into the U-plane buffer, it writes too many bytes:

  • Y plane bytes: (W \times H)
  • U plane bytes: ((W/2) \times (H/2))
  • Overflow bytes: (W \times H - (W/2 \times H/2) = 0.75 \times W \times H)

Concrete example tried (150×64):

  • Y: (150 \times 64 = 9600) bytes
  • U: (75 \times 32 = 2400) bytes
  • Overflow: (9600 - 2400 = 7200) bytes past the end of the U allocation

This is a heap buffer overflow into whatever allocations follow the U-plane buffer in the decoder’s heap layout (structures, metadata, other buffers, etc.). The exact victims depend on build + runtime allocator layout.


The Exploit Chain

Vuln 1: PIL BytesIO Address Leak (ASLR Bypass)

When you send an invalid image to vLLM's multimodal endpoint, PIL throws an error like:

cannot identify image file <_io.BytesIO object at 0x7a95e299e750>
                                                   ^^^^^^^^^^^^^^^^
                                                   LEAKED ADDRESS!

vLLM returns this error to the client, leaking a heap address. This address is ~10.33 GB before libc in memory. With this leak, we reduce ASLR from 4 billion guesses to ~8 guesses.

Vuln 2: JPEG2000 cdef Heap Overflow (RCE)

vLLM uses OpenCV (cv2) to decode videos. OpenCV bundles FFmpeg 5.1.x which has a heap overflow in the JPEG2000 decoder. The OpenCV is used for video decoding so if we build a video from JPEG2000 frames it will reach the vuln:

vLLM API Request to Completions/Invocation
     ↓
OpenCV cv2.VideoCapture()
     ↓
FFmpeg 5.1 (bundled in OpenCV)
     ↓
JPEG2000 decoder (libopenjp2)
     ↓
HEAP OVERFLOW via malicious "cdef" box
     ↓
Overwrite function pointer → RCE!

How the overflow works:

  • JPEG2000 has a cdef box that remaps color channels
  • We remap Y (luma) into the U (chroma) buffer
  • Y plane = 9,600 bytes, U plane = 2,400 bytes
  • On small geometry like 150x64 pixel image we get 7,200 bytes overflow past the U buffer. We can grow that exponentially by making bigger images.
  • This overwrites an AVBuffer structure containing a free() function pointer. This could be any function pointer or other targets.
  • We set free = system() and opaque = "command string"
  • When the buffer is freed → system("our command") executes

vLLM Attack Surface

Affected Endpoints

Both multimodal endpoints are vulnerable:

POST /v1/chat/completions     (with video_url in content)
POST /v1/invocations          (with video_url in content)

Request Flow

1. Attacker sends request with video_url pointing to malicious .mov file
2. vLLM fetches the video from the URL
3. vLLM passes video bytes to cv2.VideoCapture()
4. OpenCV's bundled FFmpeg decodes JPEG2000 frames
5. Malicious cdef box triggers heap overflow
6. AVBuffer.free pointer overwritten with system()
7. When buffer is released → system("attacker command") executes

Versions Affected

ComponentVersionNotes
vLLM>= 0.8.3, < 0.14.1Default config vulnerable when serving a video model
OpenCV (cv2)4.x with FFmpeg bundleBundled FFmpeg is vulnerable
FFmpeg5.1.x (bundled)JPEG2000 cdef overflow
libopenjp22.xHonors malicious cdef box

Fixes

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIvllm0.8.3&&< 0.14.10.14.1pip install --upgrade 'vllm==0.14.1'

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update vllm to 0.14.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-22778 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2026-22778 can be triaged on real exposure rather than presence alone.

Tailored to CVE-2026-22778. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

How to detect CVE-2026-22778

A community-maintained Nuclei template exists for this CVE. You can scan for it directly:

nuclei -id cve-2026-22778 -u https://target
Template
vLLM 0.8.3 - 0.14.0 - Information Disclosure
Severity
critical
Impact
Remote attackers can leak heap addresses, significantly reducing ASLR effectiveness and enabling further exploitation like remote code execution.
Remediation
Upgrade to version 0.14.1 or later.

Template by ProjectDiscovery nuclei-templates (kenlacroix), MIT licensed. View the full template. Scan only systems you are authorised to test.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Red HatCritical

This vulnerability is rated Critical rather than Important because it allows unauthenticated remote code execution without requiring user interaction, ultimately leading to full compromise of the affected system. An attacker can provide a malicious video URL to a vulnerable vLLM inference endpoint, which causes the…

ProductFixed inAdvisory
Red Hat AI Inference Server 3.2rhaiis/vllm-cuda-rhel9:1772160593RHSA-2026:3461
Red Hat AI Inference Server 3.2rhaiis/vllm-rocm-rhel9:1772160625RHSA-2026:3462
Red Hat AI Inference Server 3.3rhaiis/vllm-spyre-rhel9:1782352919RHSA-2026:30087
Red Hat AI Inference Server 3.3rhaiis/vllm-rocm-rhel9:1782353093RHSA-2026:30088
Red Hat AI Inference Server 3.3rhaiis/vllm-cuda-rhel9:1782352847RHSA-2026:30089
Red Hat OpenShift AI 2.25rhoai/odh-vllm-cpu-rhel9:1772093436RHSA-2026:3782
Red Hat OpenShift AI 3.3rhoai/odh-vllm-cpu-rhel9:1778264363RHSA-2026:19712
Red Hat OpenShift AI 3.3rhoai/odh-vllm-gaudi-rhel9:1770956034RHSA-2026:3713

Frequently Asked Questions

## Summary **A chain of vulnerabilities in vLLM allow Remote Code Execution (RCE):** 1. **Info Leak** - PIL error messages expose memory addresses, bypassing ASLR 2. **Heap Overflow** - JPEG2000 decoder in OpenCV/FFmpeg has a heap overflow that lets us hijack code execution **Result:** Send a malicious video URL to vLLM Completions or Invocations **for a video model** -> Execute arbitrary commands on the server Completely default vLLM instance directly from pip, or docker, does not have authentication so "None" privileges are required, but even with non-default api-key enabled configuratio
O3 Security · Impact-Aware SCA

Is CVE-2026-22778 in your dependencies?

O3 Security finds CVE-2026-22778 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

CVE-2026-22778: vllm RCE (Critical 9.8) | O3 Security