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Response snapshot Oct 27, 2026, 03:03 UTC

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VulnerabilityCVE-2026-112894

Pillow: Quadratic-time DoS in PDF save_all (PdfParser.next_object_id)

``` ============================================================================ Quadratic-time DoS in PDF save_all (PdfParser.next_object_id) Location: src/PIL/PdfParser.

Published Updated

At a glance

Exploited
Not known
Severity
8.7 High (CVSS 4.0)
Chance of exploitation
0.5% in the next 30 days
Sources
2 independent sources

What to do

GitHub lists first patched versions: Pillow: 13.0.0. Confirm the affected range and upgrade guidance in the exact advisory.

Read GitHub-reviewed advisory GHSA-jg86-v3jv-hrpm (opens a new tab)

What happened

``` ============================================================================ Quadratic-time DoS in PDF save_all (PdfParser.next_object_id) Location: src/PIL/PdfParser.py:629 in next_object_id() Commit: a707c6b21c313880d0771e54965d142dd42358b6 ============================================================================ ```

Pillow's PDF writer takes time proportional to the square of the number of frames when it saves a multi-frame image as a multi-page PDF. Anyone who can get an application to convert their uploaded image this way can tie up a worker at full CPU for minutes with a small file.

A 240 KB animated image takes about 70 seconds, a 720 KB one an estimated 10 minutes, and a 125-byte animated PNG that merely *declares* a large frame count triggers the same slowdown with no upper bound short of 2³¹ frames. Memory stays under 100 MB and the decompression-bomb limit never applies.

The cause is `PdfParser.next_object_id()` in `src/PIL/PdfParser.py`, which finds the next free id by computing `max(self.xref_table.keys())`. `XrefTable.keys()` rebuilds three sets over every id reserved so far on each call. `PdfImagePlugin._save()` calls it three times (four for palette images with transparency) per frame, for `n_frames` frames, before any page is written.

Continue reading

That makes the id-reservation loop alone O(N²). Because `n_frames` is taken from the file before any frame is seeked, APNG's `acTL` count drives the loop even when the frames don't exist. In that case the save raises `OSError` only after the full delay.

The precondition is that the application calls `im.save(..., "PDF", save_all=True)` on the attacker's file; `Image.open()` and `load()` alone do not reach this code. The attached reproducer shows 4× the frames costing about 11× the CPU for a valid GIF and about 16× for a fixed 125-byte APNG. The behaviour was introduced in `6207b44ab1ff` (2018-01-18) and first shipped in Pillow 5.1.0. A small patch that tracks the highest id in `XrefTable` makes the save linear.

## Details

Saving a multi-frame image as a multi-page PDF (`im.save(fp, "PDF", save_all=True)`) costs CPU time proportional to the square of the frame count, independent of pixel content, because every object id the PDF writer reserves is found by rebuilding and scanning the set of all ids reserved so far.

A 1×1-pixel animated GIF made of N identical 15-byte frames (built by the reproducer) gives, on a 2-CPU container with the stock build at this commit (absolute timings vary by machine; the growth rate does not):

| frames | GIF size | `im.save(BytesIO(), "PDF", save_all=True)` | peak RSS | |---|---|---|---| | 2 000 | 30 KB | 1.1 s | | | 8 000 | 120 KB | 14.7 s | | | 16 000 | 240 KB | 70 s | 60 MB | | 32 000 | 480 KB | 262 s | 95 MB | | 48 000 | 720 KB | still running at 120 s (extrapolated ≈ 10 min) | |

Each doubling of the input roughly quadruples the time. `cProfile` of the 8 000-frame save attributes 13.5 s of 17.6 s to 24 003 calls of `PdfParser.next_object_id` (8.9 s inside `XrefTable.keys`, 3.9 s inside `max`); encoding and writing the 8 000 pages is the remaining ~4 s and scales linearly.

The per-page id reservation loop runs before any page is written:

```python # src/PIL/PdfImagePlugin.py:247-259 (_save) for im in ims: im_number_of_pages = 1 if save_all: im_number_of_pages = getattr(im, "n_frames", 1) … for i in range(im_number_of_pages): image_refs.append(existing_pdf.next_object_id(0)) if im.mode == "P" and "transparency" in im.info: image_refs.append(existing_pdf.next_object_id(0))

page_refs.append(existing_pdf.next_object_id(0)) contents_refs.append(existing_pdf.next_object_id(0)) ```

`n_frames` comes straight from the file (for GIF it is the number of image blocks; the GIF plugin counts them by walking block headers, which is cheap). The frames do not even have to exist: the loop runs on `n_frames` before any frame is seeked, and for APNG `n_frames` is the `num_frames` value declared in the `acTL` chunk, rejected only if it is 0 or above `0x80000000` (`src/PIL/PngImagePlugin.py:716-720`).

A 125-byte APNG declaring 8 000 frames therefore costs the same ~10–13 s as the 120 KB GIF, and more without bound as the declared count grows. Each of the 3 (or 4, for `P` images with transparency) calls per frame lands here:

```python # src/PIL/PdfParser.py:626-634 def next_object_id(self, offset: int | None = None) -> IndirectReference: try: # TODO: support reuse of deleted objects reference = IndirectReference(max(self.xref_table.keys()) + 1, 0) except ValueError: reference = IndirectReference(1, 0) if offset is not None: self.xref_table[reference.object_id] = (offset, 0) return reference

# src/PIL/PdfParser.py:175-178 (XrefTable) def keys(self) -> set[int]: return ( set(self.existing_entries.keys()) - set(self.deleted_entries.keys()) ) | set(self.new_entries.keys()) ```

`_save` passes `offset=0`, so every reserved id is inserted into `xref_table.new_entries`. The k-th call therefore materialises three sets covering all k−1 previous ids, computes a difference and a union, and then `max()` scans the result — O(k) work to produce the integer k.

Summed over 3N calls this is O(N²): for 48 000 frames, 144 002 calls over sets growing to 144 002 elements, on the order of 10¹⁰ set-element operations, all spent before `_write_image` is reached for the first frame.

Nothing on this path consults `MAX_IMAGE_PIXELS` (the frames are 1×1) and nothing raises; for the GIF the save eventually completes and produces a valid PDF, and for the declared-only APNG it raises `OSError` only once the id loop has finished and the first missing frame is seeked.

This reservation scheme was introduced in `6207b44ab1ff` ("issue #2959: support appending to existing PDFs", 2018-01-18) and first shipped in Pillow 5.1.0; before it, the writer numbered objects arithmetically.

For contrast, `write_obj(ref, …)` with an already-reserved `ref` (`src/PIL/PdfParser.py:548-555`) only does a dict store, which is why the page-wr

Affected package ranges reported by GitHub: Pillow: >= 5.1.0, < 13.0.0

Scores and how they were measured

CVSS base severity

8.7

Version 4.0As of

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

Measurement source (opens a new tab)

How bad it is if exploited. Not a prediction of exploitation or proof you are exposed.

EPSS probability

0.5%

Scored Percentile 43.4%

Model and measurement source (opens a new tab)

A model's estimate of exploitation in the next 30 days, not certainty or your own risk.

Related technology

Sources

  1. Primary source

    GitHub-reviewed advisory GHSA-jg86-v3jv-hrpm (opens a new tab)

    The public advisory supplies the description, affected package/version ranges, and any listed CVSS metric. GitHub review is not local review.

    Published Checked

  2. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  3. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  4. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  5. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  6. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  7. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  8. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  9. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  10. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  11. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-jg86-v3jv-hrpm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  12. Primary source

    NVD CVE-2026-112894 (opens a new tab)

    NVD record: description, CVSS (the CNA score is preferred), affected products, and publication date. Not evidence of exploitation.

    Published Checked

  13. Primary source

    NVD CVE API 2.0 (opens a new tab)

    The NVD API page that supplied this CVE record.

    Published Date not suppliedChecked

  14. Primary source

    FIRST measured EPSS score (opens a new tab)

    Measured EPSS probability and percentile for CVE-2026-112894, scored 2026-10-26. This is a model estimate, not proof of exposure.

    Published Checked

Cite this record

For factual claims, cite the primary sources above; cite KEVWire for its compilation, dates, and classifications.

APA

KEVWire. (2026, October 21). Pillow: Quadratic-time DoS in PDF save_all (PdfParser.next_object_id) (CVE-2026-112894). https://kevwire.com/record/CVE-2026-112894

MLA

"Pillow: Quadratic-time DoS in PDF save_all (PdfParser.next_object_id) (CVE-2026-112894)." KEVWire, 21 Oct. 2026, kevwire.com/record/CVE-2026-112894.

Chicago

KEVWire. "Pillow: Quadratic-time DoS in PDF save_all (PdfParser.next_object_id) (CVE-2026-112894)." Last modified October 21, 2026. https://kevwire.com/record/CVE-2026-112894.

Evidence summary (plain text)

Pillow: Quadratic-time DoS in PDF save_all (PdfParser.next_object_id) (Oct 21, 2026).
```
============================================================================
Quadratic-time DoS in PDF save_all (PdfParser.next_object_id)
Location: src/PIL/PdfParser.py:629 in next_object_id()
Commit: a707c6b21c313880d0771e54965d142dd42358b6
============================================================================
```

Pillow's PDF writer takes time proportional to the square of the number of frames when it saves a multi-frame image as a multi-page PDF. Anyone who can get an application to convert their uploaded image this way can tie up a worker at full CPU for minutes with a small file. A 240 KB animated image takes about 70 seconds, a 720 KB one an estimated 10 minutes, and a 125-byte animated PNG that merely *declares* a large frame count triggers the same slowdown with no upper bound short of 2³¹ frames. Memory stays under 100 MB and the decompression-bomb limit never applies.
CVSS 8.7 (version 4.0; evidence as of Oct 21, 2026; https://github.com/advisories/GHSA-jg86-v3jv-hrpm).
EPSS 0.5% for the following 30 days, scored Oct 26, 2026 (https://api.first.org/data/v1/epss?cve=CVE-2026-112894&date=2026-10-26).
Sources:
GitHub-reviewed advisory GHSA-jg86-v3jv-hrpm: https://github.com/advisories/GHSA-jg86-v3jv-hrpm
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMlQxNTo1Mjo1NlrOAAfxiA%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMlQxNDowNjoxNFrOAAfxZw%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMlQwODoyNzoyM1rOAAYp9A%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMVQyMTozNTo1OFrOAAfwCQ%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMVQyMTozMDoxM1rOAAfv2A%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMVQyMToyODowOVrOAAfv1g%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yMVQyMToyNDoyMFrOAAfv1Q%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yM1QxODozNTowMVrOAAZRcw%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yM1QxODozMDoyN1rOAAY_5Q%3D%3D
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc&after=Y3Vyc29yOnYyOpK0MjAyNi0xMC0yM1QxODoyOTowNVrOAAfz8g%3D%3D
NVD CVE-2026-112894: https://nvd.nist.gov/vuln/detail/CVE-2026-112894
NVD CVE API 2.0: https://services.nvd.nist.gov/rest/json/cves/2.0?pubStartDate=2026-10-12T13:35:55.142Z&pubEndDate=2026-10-26T13:35:55.142Z&resultsPerPage=500&startIndex=5500
FIRST measured EPSS score: https://api.first.org/data/v1/epss?cve=CVE-2026-112894&date=2026-10-26
Record ID: CVE-2026-112894.
Record origin: Ingested record.
AI evidence: Not assessed.
Known exploitation: Not assessed.
Zero-day status: Not assessed. KEV membership alone does not establish zero-day status.
About this data

This record was ingested from a data source. Retrieved: Oct 27, 2026, 01:40 UTC. Ingestion is not an independent editorial investigation.

A CISA KEV listing documents known exploitation, not necessarily a zero-day; absence from the catalog does not prove a vulnerability has never been exploited.

Technology tags indicate possible relevance, not verified exposure. Confirm the affected product, version, and configuration against your own inventory; KEVWire does not scan your systems.

A personal reading checklist on this device, not verified remediation.

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