CVE-2024-20439 (GCVE-0-2024-20439)
Vulnerability from cvelistv5 – Published: 2024-09-04 16:28 – Updated: 2025-10-21 22:55Summary
A vulnerability in Cisco Smart Licensing Utility (CSLU) could allow an unauthenticated, remote attacker to log into an affected system by using a static administrative credential.
This vulnerability is due to an undocumented static user credential for an administrative account. An attacker could exploit this vulnerability by using the static credentials to login to the affected system. A successful exploit could allow the attacker to login to the affected system with administrative rights over the CSLU application API.
Severity
9.8 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-03-31 18:12 UTC
CWE
- CWE-912 - Hidden Functionality
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://sec.cloudapps.cisco.com/security/center/c… | |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Cisco | Cisco Smart License Utility |
Affected:
2.1.0
Affected: 2.0.0 Affected: 2.2.0 |
|
| cisco | cisco_smart_license_utility |
Affected:
2.1.0
Affected: 2.0.0 Affected: 2.2.0 cpe:2.3:a:cisco:cisco_smart_license_utility:*:*:*:*:*:*:*:* |
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 2bfb1889-8407-4e8b-a20b-0b94c20f13f1
Exploited: Yes
Timestamps
First Seen: 2025-03-31
Asserted: 2025-03-31
Scope
Notes: Cisco Smart Licensing Utility Static Credential Vulnerability | Affected: Cisco / Smart Licensing Utility | Description: Cisco Smart Licensing Utility contains a static credential vulnerability that allows an unauthenticated, remote attacker to log in to an affected system and gain administrative credentials. | Required action: Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable. | Due date: 2025-04-21 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-cslu-7gHMzWmw ; https://nvd.nist.gov/vuln/detail/CVE-2024-20439
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | CWE-912 |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | Smart Licensing Utility |
| Due Date | 2025-04-21 |
| Date Added | 2025-03-31 |
| Vendorproject | Cisco |
| Vulnerabilityname | Cisco Smart Licensing Utility Static Credential Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-10-02 08:09 CEST
| Updated: 2026-10-02 08:09 CEST
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: dcd87324-96fe-4379-8b1e-a74babf79a66
Exploited: Yes
Timestamps
First Seen: 2025-03-31
Asserted: 2025-03-31
Scope
Notes: A vulnerability in Cisco Smart Licensing Utility (CSLU) could allow an unauthenticated, remote attacker to log into an affected system by using a... | Affected: Cisco / Cisco Smart License Utility | CVSS: 9.8 (CRITICAL) | EPSS: 0.9709 | Used in malware: unknown | Not yet in CISA KEV: False
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | A vulnerability in Cisco Smart Licensing Utility (CSLU) could allow an unauthenticated, remote attacker to log into an affected system by using a... |
| Cve Id | CVE-2024-20439 |
| Vendor | Cisco |
| Ghsa Id | None |
| Product | Cisco Smart License Utility |
| Added Date | 2025-03-31T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.9709 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99892 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2024-20439 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | False |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 CEST
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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.
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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.
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