NASA CFITSIO 3.42 Library ffgphd/ffgtkn FIT Image memory corruption

CVSS Meta Temp Score
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Current Exploit Price (≈)
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CTI Interest Score
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8.0$0-$5k0.00

Summaryinfo

A vulnerability was found in NASA CFITSIO 3.42. It has been rated as critical. The impacted element is the function ffgphd/ffgtkn of the component Library. Performing a manipulation as part of FIT Image results in memory corruption. This vulnerability is reported as CVE-2018-3846. The attack is possible to be carried out remotely. No exploit exists.

Detailsinfo

A vulnerability was found in NASA CFITSIO 3.42. It has been classified as critical. This affects the function ffgphd/ffgtkn of the component Library. The manipulation as part of a FIT Image 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 the ffgphd and ffgtkn functions in NASA CFITSIO 3.42, specially crafted images parsed via the library can cause a stack-based buffer overflow overwriting arbitrary data. An attacker can deliver an FIT image to trigger this vulnerability and potentially gain code execution.

The bug was discovered 04/12/2018. The weakness was disclosed 04/16/2018 (Website). The advisory is shared at talosintelligence.com. This vulnerability is uniquely identified as CVE-2018-3846 since 01/02/2018. 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 are known, but no exploit is available.

The vulnerability was handled as a non-public zero-day exploit for at least 4 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 entries VDB-116279 and VDB-116278 are pretty similar. If you want to get the best quality for vulnerability data then you always have to consider VulDB.

Productinfo

Vendor

Name

Version

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔍
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 8.0
VulDB Meta Temp Score: 8.0

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

NVD Base Score: 8.8
NVD Vector: 🔍

CNA Base Score: 8.8
CNA Vector (Talos): 🔍

CVSSv2info

AVACAuCIA
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VectorComplexityAuthenticationConfidentialityIntegrityAvailability
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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: 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

01/02/2018 🔍
04/12/2018 +100 days 🔍
04/16/2018 +3 days 🔍
04/16/2018 +0 days 🔍
04/17/2018 +1 days 🔍
02/28/2023 +1778 days 🔍

Sourcesinfo

Advisory: FEDORA-2019-3c1aed2aa9
Status: Not defined

CVE: CVE-2018-3846 (🔍)
GCVE (CVE): GCVE-0-2018-3846
GCVE (VulDB): GCVE-100-116277
See also: 🔍

Entryinfo

Created: 04/17/2018 09:51
Updated: 02/28/2023 10:13
Changes: 04/17/2018 09:51 (59), 01/25/2020 15:28 (1), 02/28/2023 10:07 (3), 02/28/2023 10:13 (12)
Complete: 🔍
Cache ID: 216::103

If you want to get the best quality for vulnerability data then you always have to consider VulDB.

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