CVSS v4.0 Explained: What’s New, Why It Matters, and How It’s Used

 CVSS v4.0 Explained in Detail

What is CVSS v4.0?

CVSS v4.0 (released November 1, 2023) is the latest version of the Common Vulnerability Scoring System, an open standard used globally to communicate the severity of software, hardware, and firmware vulnerabilities.

It provides a numerical severity score from 0 to 10 and a corresponding vector string that explains how the score was calculated.

CVSS v4.0 introduces changes to improve granularity, accuracy, flexibility, and real‑world relevance in vulnerability scoring.

CVSS v4.0 Metric Groups

CVSS v4.0 consists of four metric groups:

Base, Threat, Environmental, and Supplemental.

1. Base Metrics

These are the intrinsic characteristics of a vulnerability, attributes that do not change across environments or over time.

They form the foundation of the CVSS score.

Key updates in CVSS v4.0 Base metrics include:

• Attack Requirements (AT): New metric describing conditions needed for exploitation.
• User Interaction (UI) was expanded to None, Passive, and Active, providing finer-grained control.
• Impact metrics revamped:

• Vulnerable System impacts (VC, VI, VA)
• Subsequent System impacts (SC, SI, SA)
• These replace “Scope” from CVSS v3.1.

2. Threat Metrics

These describe real‑world exploitation conditions that can change over time, such as exploit availability and active attacks.

They now replace the Temporal metrics in CVSS v3.1. 

They allow organizations to calculate a more realistic severity based on:

• in‑the‑wild attacks
• existence of exploit code
• technical maturity of exploits

3. Environmental Metrics

These represent the unique characteristics of the environment where a vulnerability exists.

They help organizations tailor scores to their infrastructure. 

Examples include:

• system value
• controls in place
• business impact
• compensating security mechanisms

4. Supplemental Metrics (New)

A brand‑new group providing additional context without modifying the numeric score.

This includes information such as safety‑related impacts or automation‑relevant data. [first.org]

These metrics are useful for:

• medical device cybersecurity (e.g., FDA recognition) 
• industrial systems
• compliance reporting
• fine‑grained prioritization

Qualitative Severity Ratings (v4.0)

According to NVD, CVSS v4.0 uses:

• Low: 0.1–3.9
• Medium: 4.0–6.9
• High: 7.0–8.9
• Critical: 9.0–10.0

Key Improvements Over CVSS v3.1

1. Better Definition of User Interaction

Passive vs. Active user interaction helps distinguish:

• Passive → user only needs to be present
• Active → user must perform an action

2. Attack Requirements (AT) Metric

Separates “conditions needed to exploit” from “exploit complexity,” making scoring more precise.

 3. Removal/Replacement of Scope

CVSS v3.1’s Scope was often misunderstood.

CVSS v4.0 uses separate impact metrics for “Vulnerable System” and “Subsequent Systems.”

4. New Supplemental Metrics

These allow non‑score‑affecting context, such as safety, automation, and exploit vectorization.

 5. Better Alignment with Real‑World Exploitation

The new Threat metrics track real‑world activity more cleanly than v3’s Temporal metrics.

Why CVSS v4.0 Matters

More Accurate Severity Assessments

More precise metrics → fewer inflated or misleading scores.

Improved Prioritization

Organizations can incorporate environment- and threat‑specific data to improve remediation decisions.

Better Reporting and Compliance

Used by NVD, FIRST, cybersecurity vendors, and regulators such as the FDA.

Enhanced Granularity for Critical Infrastructure

New Supplemental metrics help sectors like healthcare, ICS/OT, and cloud services add context without modifying the core score.

How CVSS v4.0 Is Used Today

NVD (National Vulnerability Database) supports CVSS v4.0 Base scores.

(As of 2024–2025, Threat and Environmental metrics must be user‑calculated.)

Cybersecurity vendors (Qualys, Checkmarx, etc.) are adopting v4.

FDA Recognized Standard for medical device cybersecurity.

Summary

CVSS v4.0 is the most refined and flexible version of the Common Vulnerability Scoring System to date. Its four metric groups, Base, Threat, Environmental, and Supplemental, offer more nuanced scoring, real‑world relevance, and improved context compared to previous versions.

Key improvements include:

• New Attack Requirements metric
• Improved User Interaction classification
• Replacement of Scope with clearer system impact metrics
• Introduction of Supplemental Metrics
• Better alignment with threat intelligence

CVSS v4.0 provides organizations with more accurate, adaptable, and actionable vulnerability severity assessments.

Directory Brute Force Attacks Explained: How Hidden Web Paths Are Discovered

Directory Brute Force Attack?

A directory brute-force attack (also called directory enumeration, path brute-forcing, or content discovery) is a technique used in cybersecurity to identify hidden or unlinked directories and files on a web server.

These locations may not appear anywhere on the public website, but they still exist on the server, sometimes containing:

• Admin portals
• Backups
• Development endpoints
• Configuration files
• Old versions of the site
• Sensitive documents

Security testers attempt to identify these areas to detect potential misconfigurations, while attackers seek them to gain unauthorized access.

Why Directories Can Be Hidden But Accessible

Web servers store files in a folder structure, such as:

• /admin
• /backups
• /private
• /.git
• /api/v1/

Even if a site doesn’t link to these directories publicly:

• They may still be reachable if the server doesn’t block them.
• They may leak through predictable naming patterns.
• Developers sometimes forget to remove old or test folders.

Since URLs can be guessed (e.g., example.com/admin), attackers test huge numbers of possible paths to find what the server reveals.

How Directory Brute Forcing Works (High-Level Technical View)

Again, this is conceptual, not instructional.

1. A list of common directory/file names exists in the attacker’s tool or process

• These lists contain thousands of guesses based on:
• Common naming conventions (e.g., /admin, /login)
• Framework defaults (e.g., /wp-admin for WordPress)
• Backup file names (backup.zip, db_old.sql)
• Hidden directories (/.git/, /test/, /old/)

2. Each potential path is tested against the target website

The web server responds differently depending on whether the path exists:

3. Responses are analyzed

A tester looks for:

• Valid locations that the site didn’t intend to expose
• Forbidden directories that confirm a sensitive area exists
• Patterns of interest, such as staging environments

4. Discovered content may reveal vulnerabilities

Once a hidden directory is found, it could expose:

• Admin login pages
• Backup archives containing sensitive data
• Source code repositories
• Misconfigurations
• Unpatched services

Security teams then fix these issues to harden the system.

Why It Matters for Security

For defenders:

• Directory brute force testing is essential in penetration testing and web application security assessments.
• It helps identify accidental exposures before attackers find them.
• It uncovers outdated or forgotten content (“shadow IT”).

For attackers:

• They may use directory discovery to:
• Find an entry point for intrusion
• Access sensitive information
• Identify vulnerable components
• Map the structure of a website for further attacks

Common Preventive Measures

Organizations can mitigate risks by:

• Disabling directory listing on the server
• Restricting access using authentication or IP allowlists
• Using non-predictable naming for sensitive paths
• Implementing Web Application Firewalls (WAFs)
• Monitoring for unusual patterns of requests
• Removing old or unused directories

The goal is to make it harder (or impossible) for an attacker to guess sensitive paths.

Summary

A directory brute force attack is a method of systematically guessing URL paths to find hidden directories or files on a web server. It doesn’t rely on vulnerabilities, just on predictable naming patterns or forgotten resources. While it's a legitimate security testing technique, attackers also use it to uncover sensitive content.

A Comprehensive Guide to Simultaneous Authentication of Equals (SAE) in WPA3

 Simultaneous Authentication of Equals (SAE) 

SAE is a password‑authenticated key exchange (PAKE) protocol used in WPA3‑Personal Wi‑Fi networks.

It replaces the older PSK (Pre‑Shared Key) approach used in WPA2.

SAE is based on the Dragonfly key exchange protocol and provides a far more secure method for establishing encryption keys on wireless networks.

1. Why SAE Exists

Under WPA2-PSK, a weak password made the network vulnerable to:

• Offline dictionary attacks
• Attackers could capture the 4‑way handshake and brute‑force it offline without interacting with the network.
• No forward secrecy
• If the PSK was discovered later, past traffic could be decrypted.

SAE solves these problems.

2. What SAE Does

SAE provides:

• Mutual authentication
• Both the client and the access point demonstrate knowledge of the password without revealing it.
• Forward Secrecy
• The encryption keys change for each session.
• If the password leaks later, old traffic cannot be decrypted.
• Protection from Offline Cracking
• An attacker cannot capture a handshake and brute‑force it later.
• They must perform live, interactive attempts—slowing attacks drastically.
• Resistance to Passive Attacks
• Simply listening to the traffic gives no useful information about the password.

3. How SAE Works (Step-by-Step)

SAE is a two‑phase handshake:

Phase 1 – Commit Exchange

Both sides (client and AP):

1. Convert the shared Wi‑Fi password into a Password Element (PWE).

• PWE is derived from the password and the two MAC addresses.
• Ensures the handshake is unique for each client–AP pair.

2. Generate a random number (their private “secret”).

3. Compute:

• A commit scalar
• A commit element

4. Exchange these values openly over the air.

Important:

Even though the commit values are public, they cannot be used to derive the password.

Phase 2 – Confirm Exchange

Both sides:

1. Compute the shared secret key using:

• Their own private random number
• The other party’s commit element

2. Derive a session key (PMK).

3. Exchange confirm messages proving they derived the same key.

If confirm messages match → authentication succeeds.

4. Key Properties of SAE

• Offline Attack Resistance
• An attacker capturing SAE handshakes gets no password-derivable data.
• Forward Secrecy
• Keys change for every session.
• Anti-Clogging
• To prevent DoS attacks (spamming commit messages), the AP can require "anti-clogging tokens" before continuing.
• Mutual Authentication
• Both sides prove knowledge of the password.

5. How SAE Differs from WPA2‑PSK

6. Where SAE Is Used

SAE is the mandatory authentication method for:

• WPA3-Personal
• Wi-Fi Enhanced Open (for upgrade paths)
• Enterprise environments that enable "Transition Mode"

7. Common Terms Related to SAE

• Dragonfly Key Exchange — underlying cryptographic design.
• Password Element (PWE) — ECC point representing the password.
• Commit & Confirm messages — two-step handshake communication.
• PMK (Pairwise Master Key) — key derived from SAE for the 4‑way handshake.

8. Why SAE Is Considered Secure

Because SAE:

• Never transmits information usable to guess the password
• Requires an attacker to interact for every guess
• Uses elliptic-curve Diffie-Hellman
• Uses strong hashing of the PWE
• Provides fresh keys per session

This combination makes it substantially more secure than WPA2-PSK.

Summary

SAE (Simultaneous Authentication of Equals) is the WPA3 authentication method designed to prevent:

• Offline dictionary attacks
• Decryption of old traffic
• Reuse of stale session keys
• Weaknesses inherent to WPA2-PSK

It accomplishes this through a secure, mutual, password-authenticated key exchange that provides forward secrecy and robust resistance to brute-force attacks.

SY0-701 Exam prep questions

 YOU DO NOT NEED TO USE YOUR EMAIL ADDRESS TO TAKE THIS QUIZ.

Understanding CYOD: The Enterprise Model That Blends Flexibility and Control

 CYOD (Choose Your Own Device)

What Is CYOD (Choose Your Own Device)?

CYOD (Choose Your Own Device) is an enterprise mobility strategy in which an organization offers employees a pre-approved list of devices (laptops, tablets, smartphones) and allows them to choose the model they prefer.

The key idea: employees get choice, but the company maintains control.

In a CYOD program:

• The company buys or leases the devices, or in some cases subsidizes them.
• The employee chooses from a controlled selection of hardware.
• The IT department manages the devices for security, compliance, and support.
• The devices are registered, secured, and maintained as corporate assets.

This creates a balance between employee freedom and organizational security.

Why CYOD Exists

With the rise of mobile work, companies needed a way to support:

• Employee preference for modern devices
• Corporate security requirements
• Standardized IT support
• Efficient lifecycle management

CYOD emerged as a middle ground between two extremes:

BYOD (bring any device you own)

COBO (corporate-owned, business-only, no choice)

How CYOD Works

1. IT Defines the Approved Device List

IT teams choose devices based on:

• Security capabilities
• Operating system versions
• Enterprise feature support
• Vendor relationships
• Budget

\Example device lists:

• Smartphones: iPhone 15, Samsung Galaxy S24, Google Pixel 9
• Laptops: Dell Latitude, HP EliteBook, MacBook Air/Pro
• Tablets: iPad, Surface Pro

2. Employees Select Their Preferred Device

Employees choose from the list based on:

• Familiarity
• Comfort
• Performance needs
• Accessibility requirements

3. Devices Are Configured and Secured

IT handles:

• OS hardening
• MDM enrollment (e.g., Intune, MobileIron, VMware Workspace ONE)
• Encryption
• Compliance settings
• Company apps installation

4. Device Lifecycle Management

IT manages:

• Warranty and repairs
• Software updates
• Security monitoring
• Replacement cycles (typically 2–4 years)

Benefits of CYOD

1. Stronger Security and Compliance

Since devices are standardized and IT-approved:

• Fewer vulnerabilities
• Consistent patching
• Controlled OS versions
• Easier compliance with regulations (HIPAA, GDPR, PCI-DSS, etc.)

2. Better IT Support

With fewer device variations, support teams can:

• Troubleshoot faster
• Maintain shared device images
• Use unified MDM policies

3. Higher Employee Satisfaction

Employees still get:

• A device they like
• Freedom to choose between brands/styles
• Modern, high‑quality hardware

4. Cost Control

Organizations can negotiate bulk pricing, manage warranties, and plan refresh cycles efficiently.

Challenges of CYOD

1. Higher Cost Than BYOD

Because companies still purchase or subsidize the devices.

2. Limited Personalization

Employees must choose only from the approved list.

3. Device Management Overhead

IT still must:

• Manage device inventory
• Maintain MDM tools
• Provide support

4. Balancing Choice With Standardization

Too many device options can overwhelm IT; too few options frustrate employees.

CYOD vs. BYOD vs. COPE vs. COBO

CYOD strikes a balance between user freedom and enterprise control.

When Companies Use CYOD

CYOD works especially well for:

• Organizations with strict security needs but still want modern UX
• Remote or hybrid workplaces
• Companies with large mobile workforces
• Businesses want consistent hardware standards
• Companies adopting zero‑trust security models

Industries that commonly use CYOD:

• Healthcare
• Finance
• Technology
• Government
• Education
• Manufacturing

In Summary

CYOD gives employees choice while allowing organizations to maintain strict control over hardware, security, and support.

It offers:

• Greater security than BYOD
• More flexibility than COBO
• Better user satisfaction than COPE
• Predictable support and lifecycle costs

The Hidden Biases in AI: How Data Shapes Fairness and Accuracy

 Data Bias in Artificial Intelligence

Data bias in artificial intelligence (AI) refers to systematic errors or unfair patterns that arise when the data used to train an AI system is not fully representative, is skewed, or reflects existing societal inequalities. Because AI models learn patterns from the data they are given, any bias in that data can lead to biased outcomes.

Here’s a clear breakdown:

What Causes Data Bias?

1. Historical Bias

Even if data is collected perfectly, it can still reflect past inequalities or norms.

Example: Hiring data from a company that historically hired mostly men will cause an AI résumé screener to prefer male candidates.

2. Sampling Bias

The dataset doesn't represent the full population or scenario the AI will be used for.

Example: A facial recognition system trained mostly on lighter‑skinned faces performs poorly on darker‑skinned individuals.

3. Measurement Bias

Inaccurate or inconsistent data collection affects outcomes.

Example: Using self‑reported health metrics from one demographic but clinical measurements from another.

4. Label Bias

Human annotators bring their own assumptions into the labeling process.

Example: Annotators label certain dialects of speech as “aggressive” more often.

5. Algorithmic Amplification

Even small biases in data can be amplified by feedback loops.

Example: If a predictive policing tool directs more police to certain neighborhoods, more crimes will be recorded there, reinforcing the model’s belief that those areas need more policing.

Why Data Bias Matters

Fairness Issues

Biased AI systems can unfairly penalize or discriminate against groups of people based on race, gender, age, disability, or socioeconomic status.

Accuracy Problems

Bias reduces model performance by making predictions less generalizable.

Legal & Ethical Risks

Organizations can face regulatory penalties or reputational damage if their AI systems cause harm or discrimination.

Real-World Examples

• Facial recognition models have shown higher error rates for women and people with darker skin tones.
• Automated loan approval systems have been found to give worse terms to certain demographic groups.
• Medical algorithms have sometimes underestimated risk for certain ethnic groups due to flawed data.

How to Reduce Data Bias

1. Improve Data Diversity

Ensure datasets include all relevant groups and scenarios.

2. Conduct Bias Audits

Regularly test data and models for performance disparities.

3. Use Fairness Techniques

Methods such as re-weighting, re-sampling, or algorithmic fairness constraints.

4. Increase Transparency

Document how data was collected, cleaned, and labeled (e.g., through model cards or data sheets).

5. Involve Diverse Teams

Different perspectives reduce the chance of blind spots.

In a Nutshell

Data bias in AI isn’t just a technical issue, it’s a human issue. AI mirrors the data it learns from, so creating fair and accurate systems requires attention to how data is collected, labeled, and applied.

What Is Fast Identity Online (FIDO) and How Does It Work?

 FIDO (Fast Identity Online)

Fast Identity Online (FIDO) is an open standard for online authentication that replaces traditional password-based systems with stronger, more straightforward, and more secure methods. Here’s a detailed explanation:

1. What is FIDO?

• FIDO stands for Fast Identity Online.
• It is developed by the FIDO Alliance, a consortium of tech companies (including Google, Microsoft, PayPal, etc.) focused on creating authentication standards that reduce reliance on passwords.
• The goal: secure, user-friendly, interoperable passwordless authentication across devices and platforms.

2. Why FIDO Exists

• Passwords are vulnerable to phishing, credential stuffing, and data breaches.
• FIDO addresses these issues by using public key cryptography and device-based authentication, making it resistant to common attacks.

3. How FIDO Works

• Public Key Infrastructure (PKI):
• When a user registers with a service, their device creates a key pair:
• Private key: Stored securely on the user’s device.
• Public key: Shared with the service.
• Authentication:
• The service sends a challenge.
• The device signs the challenge with the private key.
• The service verifies the signature using the public key.
• No shared secrets (like passwords) are transmitted, reducing risk.

4. FIDO Protocols

• FIDO UAF (Universal Authentication Framework):
• Passwordless login using biometrics or PIN.
• FIDO U2F (Universal 2nd Factor):
• Adds a physical security key as a second factor.
• FIDO2:
• Combines WebAuthn (a W3C standard for browsers) and CTAP (Client to Authenticator Protocol).
• Enables passwordless authentication across web and mobile.

5. Key Features

• Strong Security: Based on asymmetric cryptography.
• Privacy: No biometric data or private keys leave the device.
• Interoperability: Works across platforms and browsers.
• User Convenience: Supports biometrics, PINs, and hardware tokens.

6. Benefits

• Eliminates password-related risks.
• Reduces phishing and credential theft.
• Improves user experience with faster, easier login.

7. Common Use Cases

• Logging into websites without passwords.
• Multi-factor authentication using security keys.
• Enterprise authentication for employees.