VDB-128643 · CVE-2019-3574 · Issue 83

libsixel 1.8.2 loader.c load_jpeg memory corruption

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 (≈) Our analysts are monitoring exploit markets and are in contact with vulnerability brokers. The range indicates the observed or calculated exploit price to be seen on exploit markets. A good indicator to understand the monetary effort required for and the popularity of an attack.	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.
6.5	$0-$5k	0.00

Summaryinfo

A vulnerability marked as critical has been reported in libsixel 1.8.2. This impacts the function load_jpeg of the file loader.c. Performing a manipulation results in memory corruption. This vulnerability was named CVE-2019-3574. The attack needs to be approached locally. There is no available exploit.

Detailsinfo

A vulnerability was found in libsixel 1.8.2. It has been classified as critical. This affects the function load_jpeg of the file loader.c. The manipulation with an unknown input leads to a memory corruption vulnerability. CWE is classifying the issue as CWE-119. The product performs operations on a memory buffer, but it can read from or write to a memory location that is outside of the intended boundary of the buffer. This is going to have an impact on confidentiality, integrity, and availability. The summary by CVE is:

In libsixel v1.8.2, there is a heap-based buffer over-read in the function load_jpeg() in the file loader.c, as demonstrated by img2sixel.

The bug was discovered 01/02/2019. The weakness was presented 01/02/2019 (Website). It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2019-3574 since 01/02/2019. It is possible to initiate the attack remotely. No form of authentication is needed for exploitation. It demands that the victim is doing some kind of user interaction. Technical details of the vulnerability are known, but there is no available exploit.

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

See VDB-128642 for similar entry. Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Name

libsixel

Version

1.8.2

License

open-source

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔍
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 6.5
VulDB Meta Temp Score: 6.5

VulDB Base Score: 5.3
VulDB Temp Score: 5.3
VulDB Vector: 🔍
VulDB Reliability: 🔍

NVD Base Score: 7.8
NVD Vector: 🔍

CVSSv2info

AV	AC	Au	C	I	A
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💳	💳	💳	💳	💳	💳
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Vector	Complexity	Authentication	Confidentiality	Integrity	Availability
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍

NVD Base Score: 🔍

Exploitinginfo

Class: Memory corruption
CWE: CWE-119
CAPEC: 🔍
ATT&CK: 🔍

Physical: Partially
Local: Yes
Remote: Yes

Availability: 🔍
Status: Not defined

EPSS Score: 🔍
EPSS Percentile: 🔍

Price Prediction: 🔍
Current Price Estimation: 🔍

0-Day	Unlock	Unlock	Unlock	Unlock
Today	Unlock	Unlock	Unlock	Unlock

Threat Intelligenceinfo

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

Countermeasuresinfo

Recommended: no mitigation known
Status: 🔍

0-Day Time: 🔍

Timelineinfo

01/02/2019 🔍
01/02/2019 +0 days 🔍
01/02/2019 +0 days 🔍
01/02/2019 +0 days 🔍
01/03/2019 +1 days 🔍
06/22/2023 +1631 days 🔍

Sourcesinfo

Advisory: 83
Status: Not defined

CVE: CVE-2019-3574 (🔍)
GCVE (CVE): GCVE-0-2019-3574
GCVE (VulDB): GCVE-100-128643
See also: 🔍

Entryinfo

Created: 01/03/2019 12:55
Updated: 06/22/2023 19:14
Changes: 01/03/2019 12:55 (51), 04/25/2020 20:34 (6), 06/22/2023 19:14 (3)
Complete: 🔍
Cache ID: 216::103

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