CVE-2026-119194 in pyasn1
Summary
by MITRE • 10/29/2026
pyasn1 is a generic ASN.1 library for Python. From 0.3.3 until 0.6.5, schemaless container decoding stores every decoded component's TagSet in a growing set even though only one-versus-multiple tag types must be distinguished. Attacker-controlled BER, CER, or DER input contains many distinct long tag IDs whose TagSet hashes collide. Each later set insertion compares against an increasing number of earlier colliding tags in ConstructedPayloadDecoderBase._decodeComponentsSchemaless, producing quadratic CPU work while decoded values and order remain valid. Repeated inputs can exhaust decoder CPU and reduce service availability. This issue is fixed in version 0.6.5.
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Analysis
by VulDB Data Team • 10/29/2026
The pyasn1 library serves as a generic ASN.1 implementation for the Python programming language, widely utilized in network protocols such as SNMP, LDAP, and Kerberos to encode and decode structured data. A critical performance vulnerability was identified within versions ranging from 0.3.3 through 0.6.5, specifically affecting the schemaless container decoding mechanism. This flaw manifests when processing Basic Encoding Rules (BER), Canonical Encoding Rules (CER), or Distinguished Encoding Rules (DER) inputs that are crafted by an attacker to contain a high volume of distinct long tag identifiers. The core technical deficiency lies in how the library manages TagSets during the decoding process. Specifically, within the ConstructedPayloadDecoderBase._decodeComponentsSchemaless method, every decoded component's TagSet is stored in a growing set data structure. While the logical requirement for schemaless decoding only necessitates distinguishing between one-versus-multiple tag types, the implementation retains all distinct tags without optimizing storage or lookup mechanisms based on this simplified logic.
The security impact arises from hash collisions among these TagSets. When an attacker supplies input with many unique long tag IDs that result in identical hash values within Python's set data structure, the internal collision resolution mechanism is triggered. Each subsequent insertion into the set requires comparing the new element against all previously inserted elements that share the same hash bucket. Because the library continues to accumulate these colliding tags without limit or optimization, the number of comparisons grows linearly with each new component added to the payload. This results in quadratic computational complexity relative to the size of the input payload. Consequently, even moderately sized malicious inputs can trigger a disproportionate amount of CPU processing time as the decoder attempts to resolve hash collisions for every subsequent tag encountered during parsing.
From an operational perspective, this vulnerability enables a Denial of Service attack against any service that utilizes pyasn1 for decoding untrusted ASN.1 data. An attacker can craft specific BER, CER, or DER payloads designed to maximize hash collisions and force the decoder into excessive CPU consumption. Since the decoded values and their order remain valid according to the ASN.1 specification, standard validation checks may not detect the malicious nature of the input based on content alone. The primary symptom is a rapid exhaustion of available CPU resources on the host system processing the data. This resource depletion can lead to service unavailability for legitimate users, effectively causing a denial of service without requiring authentication or privilege escalation. The attack vector typically involves sending specially crafted network packets or file inputs that are processed by vulnerable applications relying on pyasn1 for ASN.1 parsing.
This vulnerability is categorized under CWE-400, which covers Uncontrolled Resource Consumption, as the application fails to properly limit the resources consumed during input processing. In terms of attack tactics, it aligns with MITRE ATT&CK technique T1496, Host-Based Denial of Service, where an adversary disrupts service availability by exhausting system resources such as CPU or memory through malformed inputs. The quadratic time complexity introduced by the hash collision handling is a classic example of algorithmic complexity attacks that exploit inefficient data structure usage in security-critical libraries.
To mitigate this vulnerability, organizations must ensure that all instances of pyasn1 are upgraded to version 0.6.5 or later, where the issue has been resolved through optimized TagSet management and hash collision handling. For systems unable to immediately upgrade, implementing input validation at the network perimeter can help filter out excessively large ASN.1 payloads before they reach the decoder. Additionally, deploying rate limiting on services that accept ASN.1 encoded data can reduce the impact of repeated malicious inputs by throttling connection rates or request volumes. Monitoring for unusual spikes in CPU usage associated with decoding processes may also aid in early detection of such attacks while mitigation strategies are being deployed.