VAR-202005-0719
Vulnerability from variot - Updated: 2024-02-13 01:09A vulnerability in the PDF archive parsing module in Clam AntiVirus (ClamAV) Software versions 0.101 - 0.102.2 could allow an unauthenticated, remote attacker to cause a denial of service condition on an affected device. The vulnerability is due to a stack buffer overflow read. An attacker could exploit this vulnerability by sending a crafted PDF file to an affected device. An exploit could allow the attacker to cause the ClamAV scanning process crash, resulting in a denial of service condition. Clam AntiVirus (ClamAV) The software contains an input verification vulnerability.Service operation interruption (DoS) It may be put into a state. Clam AntiVirus is an open source antivirus engine from the ClamAV team for detecting Trojans, viruses, malware and other malicious threats. ========================================================================== Ubuntu Security Notice USN-4370-2 May 21, 2020
clamav vulnerabilities
A security issue affects these releases of Ubuntu and its derivatives:
- Ubuntu 14.04 ESM
- Ubuntu 12.04 ESM
Summary:
Several security issues were fixed in ClamAV.
Software Description: - clamav: Anti-virus utility for Unix
Details:
USN-4370-1 fixed several vulnerabilities in ClamAV. This update provides the corresponding update for Ubuntu 12.04 ESM and 14.04 ESM.
Original advisory details:
It was discovered that ClamAV incorrectly handled parsing ARJ archives. (CVE-2020-3327)
It was discovered that ClamAV incorrectly handled parsing PDF files. (CVE-2020-3341)
Update instructions:
The problem can be corrected by updating your system to the following package versions:
Ubuntu 14.04 ESM: clamav 0.102.3+dfsg-0ubuntu0.14.04.1+esm1
Ubuntu 12.04 ESM: clamav 0.102.3+dfsg-0ubuntu0.12.04.1
This update uses a new upstream release, which includes additional bug fixes. In general, a standard system update will make all the necessary changes.
References: https://usn.ubuntu.com/4370-2 https://usn.ubuntu.com/4370-1 CVE-2020-3327, CVE-2020-3341
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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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