A visual introduction to ML | Tuning | Bias-Variance

I came across R2D3's interactive guide on machine learning basics (Parts 1 & 2) and thought it'd be useful to share. It's a visual explanation using a dataset of homes in San Francisco vs. New York for classification.

Part 1: Basics of ML and Decision Trees

• ML uses statistical techniques to identify patterns in data for predictions, e.g., classifying homes by features like elevation and price per sq ft.
• Decision trees create boundaries via if-then splits (forks) on variables, recursively building branches until patterns emerge.
• Training involves growing the tree for accuracy on known data, but overfitting can occur, leading to poor performance on unseen test data.

Part 2: Bias-Variance Tradeoff

• Models have tunable parameters (e.g., minimum node size) to control complexity.
• High bias: Overly simple models (e.g., a single-split "stump") ignore nuances, causing systematic errors.
• High variance: Overly complex models overfit to training data quirks, causing inconsistent errors on new data.
• Optimal models balance bias and variance to minimize total error; deeper trees reduce bias but increase variance.

Created by Stephanie Yee (statistician) and Tony Chu (designer) at R2D3.us. Great for intuitive understanding—check it out if interested.

Sources: 

• Part 1: A visual introduction to machine learning
• Part II: Model Tuning and the Bias-Variance Tradeoff

Reducing Memory-Related Vulnerabilities - NSA New Guidance

 

Here's the summary of the new Memory Safe Language (MSL) guidance. Why do you care? Memory safety vulnerabilities persist at alarming rates. 

Some older stats: 

• About 70% of Microsoft CVEs are memory safety issues
• 70% of Google Chromium project vulnerabilities are memory safety related
• 67% of zero-day vulnerabilities in 2021 were memory safety issues

Some newer stats. Because there's still a challenge.

• 66% of iOS CVEs are memory safety related
• 71% of macOS CVEs stem from memory safety issues

If you haven't been thinking about root cause analysis for reducing software vulnerabilities you're already behind your peers. And here is a major root cause. Memory Safe Programming Languages (MSLs) can eliminate these vulnerabilities entirely. These are programming languages designed to prevent common memory-related coding errors that malicious actors routinely exploit.

Business and Technical Benefits

All of this is interesting... but take note: 

Security Benefits: (obviously...)

• Vulnerability Elimination: Entire classes of bugs become impossible
• Reduced Attack Surface: Forces attackers to find other types of vulnerabilities
• Proactive Protection: Prevents problems during development rather than patching them later

Reliability Benefits: (good for business...)

• Fewer Crashes: Programs behave more predictably
• Better Error Messages: When problems occur, MSLs provide clearer debugging information
• Increased Uptime: More stable systems mean less downtime

Productivity Benefits: (good for the people...)

• Faster Debugging: Developers spend less time hunting memory bugs
• Focus on Features: Teams can concentrate on building functionality instead of fixing memory issues
• Reduced Emergency Patches: Fewer urgent security updates needed

Sources:

• Memory Safe Languages: Reducing Vulnerabilities in Modern Software Development
• The Case for Memory Safe Roadmaps

Databricks AI Security Framework (DASF) | Third-party Tools

Great work - Amazing work - by the team at Databricks. Nice job!

Databricks AI Security Framework (DASF) | Databricks

This link leads to a PDF that selflessly has links to a LOT of information. Thank you for including them!

Here's one such list. I'm storing it here as a quick yellow sticky. Go check out their work for more. 

Tool Category	Tool URL	Tool Description
Model Scanners	HiddenLayer Model Scanner	A tool that scans AI models to detect embedded malicious code, vulnerabilities, and integrity issues, ensuring secure deployment.
	Fickling	An open-source utility for analyzing and modifying Python pickle files, commonly used for serializing machine learning models.
	Protect AI Guardian	An enterprise-level tool that scans third-party and proprietary models for security threats before deployment, enforcing model security policies.
	AppSOC’s AI Security Testing solution	AppSOC’s AI Security Testing solution helps in proactively identifying, and assessing the risks from LLM models by automating model scanning, simulating adversarial attacks, and validating trust in connected systems, ensuring the models and ecosystems are safe, compliant, and deployment-ready
Model Validation Tools	Robust Intelligence Continuous Validation	A platform offering continuous validation of AI models to detect and mitigate vulnerabilities, ensuring robust and secure AI deployments.
	Protect AI Recon	A product that automatically validates LLM Model performance across common industry framework requirements (OWASP, MITRE/ATLAS).
	Vigil LLM security scanner	A tool designed to scan large language models (LLMs) for security vulnerabilities, ensuring safe deployment and usage.
	Garak Automated Scanning	An automated system that scans AI models for potential security threats, focusing on detecting malicious code and vulnerabilities.
	HiddenLayer AIDR	A solution that monitors AI models in real time to detect and respond to adversarial attacks, safeguarding AI assets.
	Citadel Lens	A security tool that provides visibility into AI models, detecting vulnerabilities and ensuring compliance with security standards.
	AppSOC’s AI Security Testing solution	AppSOC’s AI Security Testing solution helps in proactively identifying, and assessing the risks from LLM models by automating model scanning, simulating adversarial attacks, and validating trust in connected systems, ensuring the models and ecosystems are safe, compliant, and deployment-ready
AI Agents	Arhasi R.A.P.I.D	A platform offering rapid assessment and protection of AI deployments, focusing on identifying and mitigating security risks.
Guardrails for LLMs	NeMo Guardrails	A toolkit for adding programmable guardrails to AI models, ensuring they operate within defined safety and ethical boundaries.
	Guradrails AI	A framework that integrates safety protocols into AI models, preventing them from generating harmful or biased outputs.
	Lakera Guard	A security solution that monitors AI models for adversarial attacks and vulnerabilities, providing real-time protection.
	Robust Intelligence AI Firewall	A protective layer that shields AI models from adversarial inputs and attacks.
	Protect AI Layer	Layer provides LLM runtime security including observability, monitoring, blocking for AI Applications. The enterprise grade offering brought to you by the same team that built the industry leading open source solution LLM Guard.
	Arthur Shield	A monitoring solution that tracks AI model performance and security, detecting anomalies and potential threats in real time.
	Amazon Guardrails	A set of safety protocols integrated into Amazon's AI services to ensure models operate within ethical and secure boundaries.
	Meta Llama Guard	Meta implemented security measures to protect their Llama models from vulnerabilities and adversarial attacks.
	Arhasi R.A.P.I.D	A platform offering rapid assessment and protection of AI deployments, focusing on identifying and mitigating security risks.
DASF Validation and Assessment Products and Services	Safe Security	SAFE One makes cybersecurity an accelerator to the business by delivering the industry's only data-driven, unified platform for managing all your first-party and third-party cyber risks.
	Obsidian	Obsidian Security combines application posture with identity and data security, safeguarding SaaS.
	EQTY Labs	EQTY Lab builds advanced governance solutions to evolve trust in AI.
	AppSOC	Makes Databricks the most secure AI platform with real-time visibility, guardrails, and protection.
Public AI Red Teaming Tools	Garak	An automated scanning tool that analyzes AI models for potential security threats, focusing on detecting malicious code and vulnerabilities.
	Protect AI Recon	A product with a full suite of Red Teaming options for AI applications, including a library of common attacks, human augmented attacks, and LLM generated scans; complete with mapping to common industry frameworks like OWASP and MITRE/ATLAS.
	PyRIT	A Python-based tool for testing the robustness of AI models against adversarial attacks, ensuring model resilience.
	Adversarial Robustness Toolbox (ART)	An open-source library that provides tools to assess and improve the robustness of machine learning models against adversarial threats.
	Counterfeit	A tool designed to test AI models for vulnerabilities by simulating adversarial attacks, helping developers enhance model security.
	ToolBench	A suite of tools for evaluating and improving the security and robustness of AI models, focusing on detecting vulnerabilities.
	Giskard-AI llm scan	A tool that scans large language models for security vulnerabilities, ensuring safe deployment and usage.
	Hidden Layer - Automated Red Teaming for AI	A service that simulates adversarial attacks on AI models to identify vulnerabilities and strengthen defenses.
	Fickle scanning tools	Utilities designed to analyze and modify serialized Python objects, commonly used in machine learning models, to detect and mitigate security risks.
	CyberSecEval 3	A platform that evaluates the security posture of AI systems, identifying vulnerabilities and providing recommendations for mitigation.
	Parley	A tool that facilitates secure and compliant interactions between AI models and users, ensuring adherence to safety protocols.
	BITE	A framework for testing the security and robustness of AI models by simulating various adversarial attack scenarios.
	Purple Llama	Purple Llama is an umbrella project that over time will bring together tools and evals to help the community build responsibly with open generative AI models. The initial release will include tools and evals for Cyber Security and Input/Output safeguards but we plan to contribute more in the near future.

 

Yes. There's a Lot. The DoD Cybersecurity Policy Chart - CSIAC

The DoD Cybersecurity Policy Chart - CSIAC

Quoting directly from their website. They said it well enough.

"The goal of the DoD Cybersecurity Policy Chart is to capture the tremendous scope of applicable policies, some of which many cybersecurity professionals may not even be aware of, in a helpful organizational scheme. The use of colors, fonts, and hyperlinks is designed to provide additional assistance to cybersecurity professionals navigating their way through policy issues in order to defend their networks, systems, and data.

At the bottom center of the chart is a legend that identifies the originator of each policy by a color-coding scheme. On the right-hand side are boxes identifying key legal authorities, federal/national level cybersecurity policies, and operational and subordinate level documents that provide details on defending the DoD Information Network (DoDIN) and its assets. Links to these documents can also be found in the chart."

Training Links

Helpful post! This is from LinkedIn.

🚨 SHARE SOMEONE NEEDS IT 🚨
💥 𝐅𝐑𝐄𝐄 𝐈𝐓 𝐨𝐫 𝐂𝐲𝐛𝐞𝐫𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠!💥
Huge list of computer science resources (This one is great! Some links might not work, but I'm sure you can find them by doing a quick search) - https://lnkd.in/gQvxbypj

🔗 CompTIA Security+ - https://lnkd.in/gyFy_CG9
🔗 CISSP - https://lnkd.in/gUFjihpJ
🔗 Databases - https://lnkd.in/gWQmYwib
🔗 Penetration testing - https://lnkd.in/gAdgyY6h

🔗 Web application testing - https://lnkd.in/g5FkXWej

🔗 Weekly HackTheBox series and other hacking videos - https://lnkd.in/gztivT-D

🔗 Resources for practicing what you learned:

🔗 Network simulation software https://lnkd.in/gRMak7_x

🔗 Virtualization software https://lnkd.in/gFkyFVvF

🔗 Linux operating systems
https://lnkd.in/g2M__A5n
https://lnkd.in/gyc4R_F7
https://lnkd.in/gSiHYRNg
https://lnkd.in/g5GsUT7H

🔗 Microsoft Operating Systems
https://lnkd.in/gP3nxKpZ

🔗 Networking - https://lnkd.in/gNm8RhtS

🔗 More Networking - https://lnkd.in/ghqw2sHZ

🔗 Even More Networking - https://lnkd.in/g4fp8WFa

🐾 Linux - https://lnkd.in/g7KJBUYd

🐾 More Linux - https://lnkd.in/gUK8PU4p

🔗 Windows Server - https://lnkd.in/gWUTmN-5

🔗 More Windows Server- https://lnkd.in/gsWZQnwj

🔗 Python - https://lnkd.in/g_NpsqEM

🔗 Golang - https://lnkd.in/gmwz4ed5
🔗 Capture the flag
https://lnkd.in/gpnYs5Qj
https://www.vulnhub.com/
https://lnkd.in/gn2AEYhw
https://lnkd.in/g5FkXWej
Full credit :G M Faruk Ahmed, CISSP, CISA