VulnerabilityPublished 31 Oct 2026

CVE-2026-119194: Certsync · pyasn1 has quadratic CPU use in schemaless BER/CER/DER container decoding

Affects certsyncSource GCVE Database

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Authoritative record:CVE-2026-119194· High 7.5

Not known to be exploited · CVSS 7.5 High.

Data as of 31 Oct 2026, 16:20 UTC

CVSS 3.1 base score

7.5High

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

Thin evidence

One scorer or none

CVSS scores by source
CNA (GitHub_M)	CVSS 3.1	7.5

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 29 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 30 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
Homebrewcertsync`pkg:brew/certsync`Other threats	Affected >=0.1.4	Fixed in–

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AI-suggested

Until a fix is available, avoid passing untrusted or attacker-controlled data to schemaless BER, CER, or DER decoding in pyasn1. Consider applying input validation or limiting input complexity to mitigate CPU exhaustion risks.

Description

CVE-2026-119194 is a high-severity vulnerability in pyasn1 used by Certsync, where decoding attacker-controlled schemaless BER, CER, or DER data with many distinct long tag IDs causes quadratic CPU usage. This can lead to excessive CPU consumption and reduced service availability. The issue arises from inefficient handling of TagSet insertions during decoding. No confidentiality or integrity impacts were observed.

Impact

The impact is denial of service through CPU exhaustion when processing attacker-controlled inputs in schemaless ASN.1 decoding. There is no observed impact on confidentiality or integrity. The vulnerability affects availability by causing disproportionate CPU consumption on valid inputs with many distinct tag IDs.

Summary

The vulnerability occurs in pyasn1 version 0.6.4 during schemaless decoding of BER, CER, or DER data. The decoder inserts every decoded component's TagSet into a growing set to distinguish tag sets, but attacker-controlled tag IDs with colliding hashes cause the set to grow quadratically, increasing CPU time disproportionately. This results in CPU exhaustion and potential denial of service. The flaw is demonstrated by a proof-of-concept that shows CPU time rising significantly with increased input size. The fix involves stopping the growth of the classification set after it contains two distinct tag sets while preserving decoding correctness.

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 31 Oct 2026, 16:20 UTC

Technical details

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