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GSS message token handling
Two krb5 Vulnerabilities Fixed in Debian: Patch Your Systems Now
Rohan Timalsina
|
August 6, 2024
|
CVE-2024-37370
,
CVE-2024-37371
,
data integrity
,
Debian Security Fixes
,
Debian Security Update
,
denial of service vulnerability
,
end-of-life Linux
,
Extended Lifecycle Support
,
GSS message token handling
,
Kerberos security
,
krb5 security update
,
krb5 vulnerabilities
,
Linux & Open Source News
,
security patches
krb5, the MIT implementation of Kerberos, is a widely used protocol for network authentication. Recently, two critical vulnerabilities have been discovered in the GSS message token handling in krb5, which could allow ...
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Security in AI
Step
1
of
7
14%
How would you best describe your organization's current stage of securing the use of generative AI in your applications?
(Required)
We don’t have or use generative AI in our applications
Nothing significant yet - We have or will have generative AI in our applications but haven’t yet researched or explored the possibility of securing it.
Starting - We have begun to work on securing generative AI in our applications.
Developing - We have started to implement some security measures for generative AI.
Average - We have some policies or tools in place.
Advanced - We have some enhanced security protocols specifically tailored for generative AI in our applications.
Very advanced - We have comprehensive, mature, and proactive security solutions for generative AI in our applications.
Have you implemented, or are you planning to implement, zero trust security for the AI your organization uses or develops?
(Required)
Already implemented
Planning to implement
Considering or scoping to implement
Not considering zero trust for AI
Do not use zero trust at all
Don’t know
What are the three biggest challenges your organization faces when integrating generative AI into applications or workflows? (Select up to three)
(Required)
Cost to implement
Data security or privacy
Potential impacts on performance or scalability of the AI
Potential impacts on performance or scalability of the applications
Security of prompts or responses
Lack of expertise or experience
Integration capabilities of current systems
Access security
Intellectual property protection
Compliance with standards and regulations
How does your organization secure proprietary information used in AI training, tuning, or retrieval-augmented generation (RAG)? (Select all that apply)
(Required)
Encryption at rest
Encryption in transit
Data access control, monitoring
Data anonymization
Data/information isolation
Data masking/tokenization
Data governance policies
Zero trust / continuous authentication and authorization
No specific measures in place
Which of the following kinds of tools are you currently using to secure your organization’s use of generative AI? (select all that apply)
(Required)
Native cloud service tools (AWS Guardrails, e.g.)
Third-party tools
Custom in-house tools
No specific tools
How valuable do you think it would it be to have a solution that classifies and quantifies risks associated with generative AI tools?
(Required)
Extremely valuable
Very valuable
Somewhat valuable
Not valuable
What are, or do you think would be, the most important reasons for implementing generative AI security measures? (Select up to three)
(Required)
Regulatory compliance
Protecting intellectual property
Mitigating potential data breaches
Maintaining customer trust
Competitive advantage
Improved risk management
Increased operational efficiency
Don’t know
Δ
×
