CVE-2023-0358
published 2023-01-18CVE-2023-0358: Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
PriorityP432high7.8CVSS 3.1
AVLACLPRNUIRSUCHIHAH
EPSS
0.40%
32.6th percentile
Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
Affected
3 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | gpac | — | — |
| gpac | gpac | <= 2.2.0 | — |
| gpac | gpac_gpac | >= unspecified < 2.3.0-DEV | 2.3.0-DEV |
CVSS provenance
nvdv3.17.8HIGHCVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
nvdv3.07.8HIGHCVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
osv7.8HIGH
vendor_debian7.8HIGH
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Debian
CVE-2023-0358: gpac - Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
vendor_debian·2023·CVSS 7.8
CVE-2023-0358 [HIGH] CVE-2023-0358: gpac - Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
Scope: local
bullseye: open
OSV
CVE-2023-0358: Use After Free in GitHub repository gpac/gpac prior to 2
osv·2023-01-18·CVSS 7.8
CVE-2023-0358 [HIGH] CVE-2023-0358: Use After Free in GitHub repository gpac/gpac prior to 2
Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
GHSA
GHSA-2842-j9h8-mp66: Use After Free in GitHub repository gpac/gpac prior to 2
ghsa_unreviewed·2023-01-18
CVE-2023-0358 [HIGH] CWE-416 GHSA-2842-j9h8-mp66: Use After Free in GitHub repository gpac/gpac prior to 2
Use After Free in GitHub repository gpac/gpac prior to 2.3.0-DEV.
No detection rules found.
No public exploits indexed.
arXiv
SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
arxiv_fulltext·2025-10-22
SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
Hwiwon Lee\;
Ziqi Zhang\;
Hanxiao Lu^ \;
Lingming Zhang\;
\ ]
University of Illinois Urbana-Champaign
^ Purdue University
\ ]
\hwiwonl2, ziqi24, lingming\@illinois.edu
## Abstract
Rigorous security-focused evaluation of large language model (LLM) agents is imperative for establishing trust in their safe deployment throughout the software development lifecycle.
However, existing benchmarks largely rely on synthetic challenges or simplified vulnerability datasets that fail to capture the complexity and ambiguity encountered by security engineers in practice.
We introduce , the first fully automated benchmarking framework for evaluating LLM agents on authentic security engineering tasks.
employs a novel multi-agen
arXiv
Top of the Heap: Efficient Memory Error Protection of Safe Heap Objects
arxiv_fulltext·2024-08-19
Top of the Heap: Efficient Memory Error Protection of Safe Heap Objects
Top of the Heap: Efficient Memory Error Protection
of Safe Heap Objects
0
@IEEEauthorhalign
@IEEEauthorhalign
Kaiming Huang
Penn State University
[email protected]
Mathias Payer
EPFL
[email protected]
Zhiyun Qian
UC Riverside
[email protected]
Jack Sampson
Penn State University
[email protected]
\ \ \ \ Gang Tan
\ \ \ \ Penn State University
\ \ \ \ [email protected]
Trent Jaeger
Penn State University
[email protected]
Kaiming Huang
Penn State University
[email protected]
Mathias Payer
EPFL
[email protected]
Zhiyun Qian
UC Riverside
[email protected]
Jack Sampson
Penn State University
[email protected]
Gang Tan
Penn State University
[email protected]
Trent Jaeger
UC Riverside
[email protected]
0
CCSXML
10002978.10003022.10003023
Security and privacy Software
2023-01-18
Published