CVE-2026-59922VulnerabilityPublished 13 Aug 2026Updated 29 Oct 2026

jupyterlab · Mistune plugins/formatting: quadratic-time parsing on long runs of `x`, `==x==`, and `^^x^^` markers

Affects jupyterlabSource GCVE Database

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Authoritative record:CVE-2026-59922· High 7.5

Not known to be exploited · CVSS 7.5 High : upgrade to 4.6.1 soon.

Data as of 30 Oct 2026, 07:30 UTC

CVSS 3.1 base score

7.5High

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

Patch: Available – fixed in 4.6.1

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: None
Integrity: None
Availability: High

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

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, 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: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: None: There is no loss of confidentiality within the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: High: There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; 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 impacted component (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).

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 8 Jul 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

Upgrade jupyterlab to 4.6.1.

Affected software

Product	Affected	Fixed in
Homebrewjupyterlab`pkg:brew/jupyterlab`Other threats	Affected >=1.2.0 <4.6.1	Fixed in 4.6.1

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

Description

## Summary **Type:** Algorithmic-complexity denial of service. A run of N closed pairs `~~x~~~~x~~...` (or the analogous `==x==` for `mark`, `^^x^^` for `insert`) causes O(N²) work in the formatting parser. With the `strikethrough`, `mark`, or `insert` plugin enabled, an 8 KB input pegs the CPU for ~4 seconds; 16 KB → ~17 seconds. **File:** `src/mistune/plugins/formatting.py`, lines 13-15 (the `_STRIKE_END` / `_MARK_END` / `_INSERT_END` patterns and their per-position scan). **Root cause:** for each opening `~~`/`==`/`^^` the parser scans forward for the matching close pattern. The scan itself uses a bounded regex, but the parser tries the close-scan at every potential start position. For input shaped like `~~x~~` repeated N times, every `~~` is examined as a possible start, each scan covers up to the end of input. Total work is O(N²). Default config without these plugins handles the same input in linear time (4 ms for 4000 reps), confirming the cost is in the formatting plugin's per-marker scan, not in core parsing. ## Affected Code **File:** `src/mistune/plugins/formatting.py`, lines 12-16. ```python _STRIKE_END = re.compile(r"(?:" + PREVENT_BACKSLASH + r"\\~|[^\s~])~~(?!~)") _MARK_END = re.compile(r"(?:" + PREVENT_BACKSLASH + r"\\=|[^\s=])==(?!=)") _INSERT_END = re.compile(r"(?:" + PREVENT_BACKSLASH + r"\\\^|[^\s^])\^\^(?!\^)") # Each pattern is scanned forward from every start position fired by the # corresponding inline rule. The end-pattern itself is bounded; the cost # comes from the surrounding parser invoking the scan at every '~~' / '==' / '^^' # token in the input, giving N starts × O(N) per scan = O(N^2) total. ``` **Why it's wrong:** the same algorithmic-complexity flaw class as `[` / `[a` parsing in core: a per-token retry loop without memoisation of failed positions. Each formatting marker is tried as both a potential start and as a continuation. A linear-pass delimiter-stack algorithm (matching how `commonmark-py` and `markdown-it-py` handle emphasis) would do this work in O(N) total. The bounded regex on each individual scan does not bound the parser-level repetition. ## Exploit Chain 1. Application uses mistune to render user-supplied markdown and has any of the formatting plugins enabled (`plugins=['strikethrough']`, `['mark']`, `['insert']`, or any superset). These plugins are commonly enabled because GitHub-flavoured-Markdown compatibility requires `~~strikethrough~~` and many editors emit `==highlighting==` and `^^underline^^` shortcuts. 2. Attacker submits an 8 KB markdown payload of the form `~~x~~~~x~~~~x~~...` (40 000 characters of `~~x~~` repeated 8000 times, or the analogous shape with `==` / `^^`). 3. Server calls `mistune.create_markdown(plugins=['strikethrough'])(payload)`. CPU pegs for ~4 seconds; 16 KB → ~17 seconds; 32 KB → ~70 seconds. Pure CPU cost, no significant memory growth. 4. Repeating the request floods the worker pool. On a single-thread WSGI handler this is one request per outage; on a thread pool, a small number of concurrent attackers exhausts capacity. ## Security Impact **Severity:** sec-high. Network-reachable, no authentication, predictable scaling, single-payload primitive. Only requires a user-supplied markdown sink and a formatting plugin enabled — both are common. **Attacker capability:** small input → large CPU. Doubling input size quadruples CPU time. Sustained requests deny service to other users. **Preconditions:** application uses mistune with any of `strikethrough`, `mark`, or `insert` plugins enabled. Default config does NOT enable these (so the attack only fires against the substantial deployed population that turns them on for GFM/markdown-extra compatibility). **Differential:** PoC-verified against mistune@3.2.1: ```python import mistune, time md = mistune.create_markdown(plugins=['strikethrough']) for n in [500, 1000, 2000, 4000, 8000]: s = '~~x~~' * n t = time.time() md(s) print(f' ~~x~~ * {n} ({len(s)}b): {(time.time() - t) * 1000:.0f}ms') # Output (Python 3.13, Linux, 2.5GHz CPU): # ~~x~~ * 500 (2500b): 19ms # ~~x~~ * 1000 (5000b): 71ms # ~~x~~ * 2000 (10000b): 272ms # ~~x~~ * 4000 (20000b): 1090ms # ~~x~~ * 8000 (40000b): 4302ms # Identical scaling for `==x==` (mark) and `^^x^^` (insert): md = mistune.create_markdown(plugins=['mark']) md('==x==' * 4000) # ~1100ms md = mistune.create_markdown(plugins=['insert']) md('^^x^^' * 4000) # ~1080ms # Without the plugin, the same input parses in linear time: md = mistune.create_markdown() # no plugins md('~~x~~' * 4000) # 4ms (1000x faster) ``` The patched build (with the suggested fix below — either a delimiter-stack rewrite or a hard cap on the number of unmatched markers tracked) keeps the time linear in N. ## Suggested Fix The minimal fix is to cap the number of simultaneously-tracked unmatched markers, treating extras as literal text. The proper fix is a single-pass delimiter-stack algorithm matching the CommonMark ref

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