GL.iNet GL-AR300M-Lite 2.27 firmware_cgi command injection

CVSS Meta Temp Score
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Current Exploit Price (≈)
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CTI Interest Score
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7.2$0-$5k0.00

Summaryinfo

A vulnerability was found in GL.iNet GL-AR300M-Lite 2.27. It has been classified as critical. Affected by this vulnerability is an unknown functionality of the file firmware_cgi. The manipulation leads to command injection. This vulnerability is documented as CVE-2019-6275. The attack can be initiated remotely. Additionally, an exploit exists.

Detailsinfo

A vulnerability was found in GL.iNet GL-AR300M-Lite 2.27. It has been rated as critical. This issue affects an unknown code block of the file firmware_cgi. The manipulation with an unknown input leads to a command injection vulnerability. Using CWE to declare the problem leads to CWE-77. The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component. Impacted is confidentiality, integrity, and availability. The summary by CVE is:

Command injection vulnerability in firmware_cgi in GL.iNet GL-AR300M-Lite devices with firmware 2.27 allows remote attackers to execute arbitrary code.

The bug was discovered 01/16/2019. The weakness was released 03/21/2019 as EDB-ID 46179 as not defined exploit (Exploit-DB). It is possible to read the advisory at exploit-db.com. The identification of this vulnerability is CVE-2019-6275 since 01/14/2019. The exploitation is known to be easy. The attack may be initiated remotely. The requirement for exploitation is a simple authentication. Technical details as well as a public exploit are known. The attack technique deployed by this issue is T1202 according to MITRE ATT&CK.

After immediately, there has been an exploit disclosed. The exploit is available at exploit-db.com. It is declared as proof-of-concept. The vulnerability was handled as a non-public zero-day exploit for at least 64 days. During that time the estimated underground price was around $0-$5k.

There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.

The vulnerability is also documented in the vulnerability database at Exploit-DB (46179). Entries connected to this vulnerability are available at VDB-132008, VDB-132009 and VDB-132010. Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Vendor

Name

Version

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔍
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 7.6
VulDB Meta Temp Score: 7.2

VulDB Base Score: 6.3
VulDB Temp Score: 5.7
VulDB Vector: 🔍
VulDB Reliability: 🔍

NVD Base Score: 8.8
NVD Vector: 🔍

CVSSv2info

AVACAuCIA
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VectorComplexityAuthenticationConfidentialityIntegrityAvailability
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍

NVD Base Score: 🔍

Exploitinginfo

Class: Command injection
CWE: CWE-77 / CWE-74 / CWE-707
CAPEC: 🔍
ATT&CK: 🔍

Physical: No
Local: No
Remote: Yes

Availability: 🔍
Access: Public
Status: Proof-of-Concept
Download: 🔍

EPSS Score: 🔍
EPSS Percentile: 🔍

Price Prediction: 🔍
Current Price Estimation: 🔍

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Exploit-DB: 🔍

Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: no mitigation known
Status: 🔍

0-Day Time: 🔍
Exploit Delay Time: 🔍

Timelineinfo

01/14/2019 🔍
01/16/2019 +2 days 🔍
03/21/2019 +64 days 🔍
03/21/2019 +0 days 🔍
03/21/2019 +0 days 🔍
03/22/2019 +1 days 🔍
09/08/2024 +1997 days 🔍

Sourcesinfo

Advisory: EDB-ID 46179
Status: Not defined

CVE: CVE-2019-6275 (🔍)
GCVE (CVE): GCVE-0-2019-6275
GCVE (VulDB): GCVE-100-132011
scip Labs: https://www.scip.ch/en/?labs.20161013
See also: 🔍

Entryinfo

Created: 03/22/2019 08:35
Updated: 09/08/2024 23:41
Changes: 03/22/2019 08:35 (64), 05/18/2020 15:40 (1), 09/08/2024 23:41 (16)
Complete: 🔍
Cache ID: 216::103

Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

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