SUSE-SU-2026:22765-1
Vulnerability from csaf_suse - Published: 2026-07-20 12:15 - Updated: 2026-10-12 17:27Summary
Security update for python-pyasn1
Severity
Important
Notes
Title of the patch: Security update for python-pyasn1
Description of the patch: This update for python-pyasn1 fixes the following issues:
- CVE-2026-59884: BER/CER/DER decoder denial of service via unbounded long-form tag IDs (bsc#1271464).
- CVE-2026-59885: quadratic complexity in OBJECT IDENTIFIER and RELATIVE-OID processing allows denial of service
(bsc#1271465).
- CVE-2026-59886: uncontrolled resource consumption when converting decoded REAL values (bsc#1271466).
Patchnames: SUSE-SL-Micro-6.2-1301
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
7.5 (High)
Affected products
Recommended
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: SUSE Linux Micro 6.2:python313-pyasn1-0:0.6.1-160000.5.1.noarch | — |
Vendor Fix
|
Threats
Impact
important
7.5 (High)
Affected products
Recommended
1 product, the same list as for
CVE-2026-59884
Threats
Impact
important
7.5 (High)
Affected products
Recommended
1 product, the same list as for
CVE-2026-59884
Threats
Impact
important
References
16 references
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"text": "pyasn1 is a generic ASN.1 library for Python. Prior to 0.6.4, the BER decoder shared by the CER and DER codecs parses long-form tags by accumulating continuation octets without an upper bound on the tag ID size, allowing a crafted input to force construction of an arbitrarily large integer with CPU cost growing quadratically and to trigger unhandled ValueError exceptions in Python 3.11+ error formatting paths. Any application decoding untrusted BER, CER, or DER input is affected. This issue is fixed in version 0.6.4.",
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"text": "pyasn1 is a generic ASN.1 library for Python. Prior to 0.6.4, the BER, CER, and DER decoders process OBJECT IDENTIFIER and RELATIVE-OID values in quadratic time relative to the number of arcs, so a small crafted payload containing an OID with many arcs consumes excessive CPU per decode() call and can deny service to applications that decode untrusted ASN.1 data. The corresponding encoders have the same quadratic behavior when an application re-encodes previously decoded attacker-supplied values. This issue is fixed in version 0.6.4.",
"title": "CVE description"
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"text": "pyasn1 is a generic ASN.1 library for Python. Prior to 0.6.4, the univ.Real type converted its mantissa, base, and exponent value to a Python float using exact big-integer exponentiation. A BER, CER, or DER encoded REAL value only a few bytes long can carry a very large exponent, causing float conversion through prettyPrint(), str(), comparison, arithmetic, int(), or an explicit float() call to consume excessive CPU and memory and hang applications that decode untrusted ASN.1 data and then print, log, or compare decoded objects. This issue is fixed in version 0.6.4.",
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.