Vulnerability-Lookup

VAR-202111-0835

Vulnerability from variot - Updated: 2024-11-23 23:01

AlquistManager branch as of commit 280d99f43b11378212652e75f6f3159cde9c1d36 is affected by a directory traversal vulnerability in alquist/IO/input.py. This attack can cause the disclosure of critical secrets stored anywhere on the system and can significantly aid in getting remote code access. AlquistManager Exists in a past traversal vulnerability.Information may be obtained. Alquist is an advanced conversational Ai robot. For interesting and engaging conversations with humans on trending topics such as movies, sports, news, etc.

There is a security vulnerability in Alquist Manager, which is caused by the lack of effective filtering and escaping of path data submitted by users in the alquist/IO/input.py file in the software, resulting in a directory traversal vulnerability. No detailed vulnerability details are currently available

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CVE-2021-43495 (GCVE-0-2021-43495)

Vulnerability from cvelistv5 – Published: 2021-11-15 12:26 – Updated: 2024-08-04 03:55

Summary

Severity

No CVSS data available.

CWE

Assigner

mitre CNA under the mitre root

CNA scorecard E 29/100 over 2175 records in the last 180 days details

References

1 reference

URL	Tags
https://github.com/AlquistManager/alquist/issues/43	x_refsource_MISC

Show details on NVD website

JSON

To clipboard

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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.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

Order JSON

How durations are calculated

Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.

Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.

Publication & reservation Sightings KEV catalogs Other timestamps

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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.

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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