deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29525 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29525 Source advisory: OSV:GHSA-XM2V-8RRW-W9PM...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29524 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29524 Source advisory: OSV:GHSA-R4PJ-74MG-8868...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29523 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29523 Source advisory: OSV:GHSA-2CPX-427X-Q2C6...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29522 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29522 Source advisory: OSV:GHSA-C968-PQ7H-7FXV...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29521 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29521 Source advisory: OSV:GHSA-HR84-FQVP-48MM...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29520 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29520 Source advisory: OSV:GHSA-WCV5-QRJ6-9PFM...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29519 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29519 Source advisory: OSV:GHSA-772J-H9XW-FFP5...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29518 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29518 Source advisory: OSV:GHSA-62GX-355R-9FHG...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29517 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29517 Source advisory: OSV:GHSA-772P-X54P-HJRV...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29516 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29516 Source advisory: OSV:GHSA-84MW-34W6-2Q43...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29515 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29515 Source advisory: OSV:GHSA-HC6C-75P4-HMQ4...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29514 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29514 Source advisory: OSV:GHSA-8H46-5M9H-7553...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29512 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29512 Source advisory: OSV:GHSA-4278-2V5V-65R4...

AutoPentest-DRL - Automated Penetration Testing Using Deep Reinforcement Learning

AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning DRL techniques. The framework determines the most appropriate attack path for a given network, and can be used to execute a simulated attack on that network via penetration testing tools, such as...

Information Disclosure Vulnerability in the Behavior Awareness System of Deep Impact Technology Co.

Ltd. is a provider of products and services focused on enterprise-class security, cloud computing, IT infrastructure and Internet of Things. An information leakage vulnerability exists in the behavior-awareness system of Deep Impact Technology Co. An attacker can use this vulnerability to obtain...

Prototype Pollution in jalik/js-deep-extend

✍️ Description Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as proto, constructor and prototype. An attacker...

GHSA-V659-54CX-G4QR Prototype Pollution in deep-override

Prototype pollution vulnerability in 'deep-override' versions 1.0.0 through 1.0.1 allows an attacker to cause a denial of service and may lead to remote code execution...

Prototype Pollution in deep-override

Prototype pollution vulnerability in 'deep-override' versions 1.0.0 through 1.0.1 allows an attacker to cause a denial of service and may lead to remote code execution...

Prototype Pollution

deep-override is vulnerable to prototype pollution. An attacker is able to exploit the vulnerability to inject arbitrary properties into existing construct prototypes and modify attributes such as proto, constructor and prototype via the override function...

deep-floorplan (=0.0.0), mpunet (=0.2.9) +1 more potentially affected by CVE-2021-29582 via tensorflow-gpu (>=2.3.0 <=2.3.2)

tensorflow-gpu PYPI version =2.3.0, =1.1.0, =1.6.1 Source cves: CVE-2021-29582 Source advisory: OSV:PYSEC-2021-708...