VulnerabilityPublished 22 Oct 2026Updated 29 Oct 2026

CVE-2026-112894: Pillow · Quadratic-time DoS in PDF save_all (PdfParser.next_object_id)

Affects aiderSource GCVE Database

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Authoritative record:CVE-2026-112894· High 8.7

Not known to be exploited · CVSS 8.7 High.

Data as of 30 Oct 2026, 07:30 UTC

CVSS 4.0 base score

8.7High

How severe the flaw is on the 0–10 CVSS scale.

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

Attack Vector: Network
Attack Complexity: Low
Attack Requirements: None
Privileges Required: None
User Interaction: None
Vulnerable system confidentiality: None
Vulnerable system integrity: None
Vulnerable system availability: High
Subsequent system confidentiality: None
Subsequent system integrity: None
Subsequent system availability: None

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable system is bound to the network stack and the set of possible attackers extends beyond the other options listed below, up to and including the entire Internet. Such a vulnerability is often termed “remotely exploitable” and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: The attacker must take no measurable action to exploit the vulnerability. The attack requires no target-specific circumvention to exploit the vulnerability. An attacker can expect repeatable success against the vulnerable system.
Attack Requirements: None: The successful attack does not depend on the deployment and execution conditions of the vulnerable system. The attacker can expect to be able to reach the vulnerability and execute the exploit under all or most instances of the vulnerability.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without interaction from any human user, other than the attacker.
Vulnerable system confidentiality: None: There is no loss of confidentiality within the Vulnerable System.
Vulnerable system integrity: None: There is no loss of integrity within the Vulnerable System.
Vulnerable system availability: High: There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the Vulnerable System; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the Vulnerable System (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).
Subsequent system confidentiality: None: There is no loss of confidentiality within the Subsequent System or all confidentiality impact is constrained to the Vulnerable System.
Subsequent system integrity: None: There is no loss of integrity within the Subsequent System or all integrity impact is constrained to the Vulnerable System.
Subsequent system availability: None: There is no impact to availability within the Subsequent System or all availability impact is constrained to the Vulnerable System.

Definitions: FIRST CVSS v4.0 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 19 Oct 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 29 Oct 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponized: A Metasploit exploit module or Exploit-DB entry exists (Metasploit scanner modules do not count), or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Affected software

Product	Affected	Fixed in
Homebrewaider`pkg:brew/aider`Other threats	Affected >=0.86.1_1	Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

Description

``` ============================================================================ 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. 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(se

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