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CVE-2016-10174 (GCVE-0-2016-10174)

Vulnerability from cvelistv5 – Published: 2017-01-30 04:24 – Updated: 2025-10-21 23:55
VLAI CISA Previdian
Summary
The NETGEAR WNR2000v5 router contains a buffer overflow in the hidden_lang_avi parameter when invoking the URL /apply.cgi?/lang_check.html. This buffer overflow can be exploited by an unauthenticated attacker to achieve remote code execution.
SSVC
Exploitation: active Automatable: yes Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-04 21:00 UTC
CWE
  • n/a
  • CWE-120 - Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')
Date Public
2017-01-29 00:00
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant

Vulnerability ID: CVE-2016-10174

Status: Confirmed

Status Updated: 2022-03-25 00:00 UTC

Exploited: Yes


Timestamps
First Seen: 2022-03-25
Asserted: 2022-03-25

Scope
Notes: KEV entry: NETGEAR WNR2000v5 Router Buffer Overflow Vulnerability | Affected: NETGEAR / WNR2000v5 Router | Description: The NETGEAR WNR2000v5 router contains a buffer overflow which can be exploited to achieve remote code execution. | Required action: Apply updates per vendor instructions. | Due date: 2022-04-15 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://nvd.nist.gov/vuln/detail/CVE-2016-10174

Evidence

Type: Vendor Report

Signal: Successful Exploitation

Confidence: 80%

Source: cisa-kev


Details
Cwes CWE-119
Feed CISA Known Exploited Vulnerabilities Catalog
Product WNR2000v5 Router
Due Date 2022-04-15
Date Added 2022-03-25
Vendorproject NETGEAR
Vulnerabilityname NETGEAR WNR2000v5 Router Buffer Overflow Vulnerability
Knownransomwarecampaignuse Unknown

References

Created: 2026-02-02 13:25 UTC | Updated: 2026-02-06 07:53 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant

Vulnerability ID: CVE-2016-10174

Status: Confirmed

Status Updated: 2022-03-25 00:00 UTC

Exploited: Yes


Timestamps
First Seen: 2022-03-25
Asserted: 2022-03-25

Scope
Notes: KEVIntel entry: The NETGEAR WNR2000v5 router contains a buffer overflow in the hidden_lang_avi parameter when invoking the URL /apply.cgi?/lang_check.html. This... | Affected: NETGEAR / WNR2000v5 router | CVSS: 9.8 (CRITICAL) | Used in malware: unknown | Not yet in CISA KEV: False

Evidence

Type: Public Report

Signal: Successful Exploitation

Confidence: 70%

Source: kevintel


Details
Feed KEVIntel (kevintel.com)
Title The NETGEAR WNR2000v5 router contains a buffer overflow in the hidden_lang_avi parameter when invoking the URL /apply.cgi?/lang_check.html. This...
Vendor NETGEAR
Product WNR2000v5 router
Added Date 2022-03-25T00:00:00.000Z
Cvss Score 9.8
Epss Score None
Cvss Severity CRITICAL
Epss Percentile None
Used In Malware unknown
Ahead Of Cisa Kev None
Not Yet In Cisa Kev False

References

Created: 2026-06-23 11:13 UTC | Updated: 2026-06-23 11:13 UTC
Show details on NVD website

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}



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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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Detection rules are retrieved from Rulezet.

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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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