Siemens QMS Automotive cleartext storage in memory

CVSS Meta Temp Score
CVSS is a standardized scoring system to determine possibilities of attacks. The Temp Score considers temporal factors like disclosure, exploit and countermeasures. The unique Meta Score calculates the average score of different sources to provide a normalized scoring system.
Current Exploit Price (≈)
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CTI Interest Score
Our Cyber Threat Intelligence team is monitoring different web sites, mailing lists, exploit markets and social media networks. The CTI Interest Score identifies the interest of attackers and the security community for this specific vulnerability in real-time. A high score indicates an elevated risk to be targeted for this vulnerability.
6.3$0-$5k0.00

Summaryinfo

A vulnerability was found in Siemens QMS Automotive. It has been classified as problematic. The impacted element is an unknown function. Performing a manipulation results in cleartext storage in memory. This vulnerability is cataloged as CVE-2022-43958. There is no exploit available.

Detailsinfo

A vulnerability was found in Siemens QMS Automotive (unknown version). It has been classified as problematic. This affects an unknown code block. The manipulation with an unknown input leads to a cleartext storage in memory vulnerability. CWE is classifying the issue as CWE-316. The product stores sensitive information in cleartext in memory. This is going to have an impact on confidentiality. The summary by CVE is:

A vulnerability has been identified in QMS Automotive (All versions). User credentials are stored in plaintext in the database. This could allow an attacker to gain access to credentials and impersonate other users.

The weakness was shared 11/08/2022 as ssa-587547. It is possible to read the advisory at cert-portal.siemens.com. This vulnerability is uniquely identified as CVE-2022-43958 since 10/27/2022. The technical details are unknown and an exploit is not publicly available. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 12/05/2022). The attack technique deployed by this issue is T1555 according to MITRE ATT&CK.

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

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

Productinfo

Vendor

Name

License

Website

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔍
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 6.3
VulDB Meta Temp Score: 6.3

VulDB Base Score: 3.5
VulDB Temp Score: 3.5
VulDB Vector: 🔍
VulDB Reliability: 🔍

NVD Base Score: 9.1
NVD Vector: 🔍

CVSSv2info

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

Exploitinginfo

Class: Cleartext storage in memory
CWE: CWE-316 / CWE-312 / CWE-310
CAPEC: 🔍
ATT&CK: 🔍

Physical: No
Local: No
Remote: Yes

Availability: 🔍
Status: Not defined

EPSS Score: 🔍
EPSS Percentile: 🔍

Price Prediction: 🔍
Current Price Estimation: 🔍

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Threat Intelligenceinfo

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

Countermeasuresinfo

Recommended: no mitigation known
Status: 🔍

0-Day Time: 🔍

Timelineinfo

10/27/2022 🔍
11/08/2022 +12 days 🔍
11/08/2022 +0 days 🔍
12/05/2022 +27 days 🔍

Sourcesinfo

Vendor: siemens.com

Advisory: ssa-587547
Status: Confirmed

CVE: CVE-2022-43958 (🔍)
GCVE (CVE): GCVE-0-2022-43958
GCVE (VulDB): GCVE-100-213099

Entryinfo

Created: 11/08/2022 17:31
Updated: 12/05/2022 14:17
Changes: 11/08/2022 17:31 (37), 12/05/2022 14:17 (12)
Complete: 🔍
Cache ID: 216::103

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

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