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CVE Record

CVE-2022-45061: An issue was discovered in Python before 3.11.1.

An issue was discovered in Python before 3.11.1. An unnecessary quadratic algorithm exists in one path when processing some inputs to the IDNA (RFC 3490) decoder, such that a crafted, unreasonably long name being presented to the decoder could lead to a CPU denial of service. Hostnames are often supplied by remote servers that could be controlled by a malicious actor; in such a scenario, they could trigger excessive CPU consumption on the client attempting to make use of an attacker-supplied supposed hostname. For example, the attack payload could be placed in the Location header of an HTTP response with status code 302. A fix is planned in 3.11.1, 3.10.9, 3.9.16, 3.8.16, and 3.7.16.

HighCVSS 7.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysishigh

Security readout for executives and security teams

Plain-English summary

This is a CPU denial-of-service flaw in Python’s IDNA hostname decoding. A malicious remote server could supply an unusually long crafted hostname, causing vulnerable Python clients to spend excessive CPU time. The practical risk is service slowdown or resource exhaustion, not data theft or code execution.

Executive priority

Treat this as a high-priority availability issue where Python handles untrusted hostnames. It is less severe than code execution, but vulnerable clients can waste CPU under attacker-controlled input. Patch through normal emergency or accelerated maintenance windows for exposed systems.

Technical view

Python versions before the fixed releases used an unnecessary quadratic algorithm in one IDNA decoder path. Processing crafted, unreasonably long names can consume excessive CPU. CVSS 3.1 is 7.5, network exploitable with high availability impact and no confidentiality or integrity impact.

Likely exposure

Exposure is most likely in Python applications or products that decode hostnames from untrusted remote responses, including clients that process attacker-controlled redirects or other supplied hostnames. Fedora and NetApp advisories indicate downstream package and product relevance.

Exploitation context

The source bundle does not show CISA KEV listing or confirmed active exploitation. The CVE description gives a plausible remote trigger path through attacker-supplied hostnames, but evidence provided supports potential CPU denial of service, not exploitation in the wild.

Researcher notes

Focus validation on IDNA decoding paths and hostname sources, especially redirect handling and remote service metadata. The provided CVE data names CWE-407 and fixed Python release targets, but does not provide complete product CPE mapping or active exploitation evidence.

Mitigation direction

  • Upgrade Python to fixed vendor-supported releases: 3.11.1, 3.10.9, 3.9.16, 3.8.16, or 3.7.16.
  • Apply operating system Python package updates from your distribution vendor.
  • Check NetApp and other appliance vendors for product-specific guidance if Python is embedded.
  • Limit processing of unusually long untrusted hostnames where application controls allow it.
  • Prioritize internet-facing services and automated clients that follow remote redirects.

Validation and detection

  • Inventory Python runtimes and distribution Python packages across servers, containers, and appliances.
  • Confirm runtime versions are at or above the fixed release for each maintained branch.
  • Review applications that parse or follow untrusted hostnames from remote responses.
  • Check vendor advisories for packaged or embedded Python dependencies.
  • Use regression tests to verify hostname-handling behavior remains functional after upgrades.
Prepared
Confidence
high
Sources
8

Public sources used

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

Conservative CVE-to-ATT&CK context

These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.

ATT&CK lookup starting points

Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.

cwe · low confidence lookup

CWE-407: Exact CWE lookup

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cve · low confidence lookup

CVE-2022-45061 mapping review

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Vulnerability profileCVE Program record
Severity
High
CVSS
7.5 (3.1)
Known Exploited
No
Published

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

Official CVE source material

CNA and ADP enrichment extracted from CVE v5

These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.

1CVSS vectors
0Timeline events
0ADP providers
37Source links

CVSS vector scores

1 official score

We collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.

ScoreVersionSeverityVectorExploitImpactSource
7.5CVSS 3.1HighCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H3.93.6Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

7.5High
CVSS 3.1 vector shape for CVE-2022-45061Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

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

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
n/an/an/aListed
Weakness

CWE details

CWE-407

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-407 · source CWE mapping

Inefficient Algorithmic Complexity

Inefficient Algorithmic Complexity represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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