PBL
Anthropic Project
AI in Cybersecurity
Explore how AI reshapes cybersecurity through adversarial attacks, defense strategies, and privacy safeguards in the era of large language models.
Project
Anthropic Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Dec. 01, 2025 - Jan. 25, 2026
Outcomes
Evaluate trade-offs in cloud vs local AI deployments
Build tools for vulnerability detection using GenAI
Deploy open-source LLMs for local, private inference
Perform privacy audits on AI systems and models
Automate tasks with LLM-powered cybersecurity tools
You Will Get
Industry Guidance
Work directly with our project leads—experts and top researchers—who bring their real-world insights and expertise straight to your learning experience.
Research Experience
Collaborate with teammates and the project lead in a multi-week project to pursue novel questions in your research field.
Peer Networks
Engage with our PBL participants from all over the world. Collaborate with new peers and learn about their own research endeavours.
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A Strong Portfolio
Put your best foot forward in the PBL with a standout project and receive a PBL Evaluation Report that can be used as a recommendation letter for employers and grad schools.
Expert Guidance
Get personalized feedback to grow your research and innovation skills.
Deliverables
Real projects, lasting connections, and new opportunities beyond your program.
Project Deliverables
The final presentation of your 8 weeks could be a poster, written report, or a slide deck, all of which can be expanded on.
Research Extension
Utilize up to 5 additional meeting times with the project lead after the project’s conclusion to build your work out for publication or conference presentation.
Industry Network
Meet peers in your projects and participate in a global talent community both online and in-person.
Industry Application
Anthropic is at the forefront of developing safe and interpretable large language models (LLMs), with a mission centered on AI alignment, robustness, and responsible deployment—making it highly relevant to this PBL. The company actively researches adversarial attacks, system alignment, and AI safety, all of which mirror the core themes of this project. By studying these challenges hands-on, students gain experience aligned with real-world industry needs.
Popular Industry Positions
Cybersecurity Analyst (AI Tools)
Leverage AI to automate security assessments and threat detection
Trust & Safety Researcher
Design safe and aligned LLM behaviors
AI Security Engineer
Defend AI systems against adversarial and privacy attacks
Tracks
Track 1
Adversarial Attacks on AI
Students will study how to bypass safety controls and explore vulnerabilities in AI models through adversarial prompting and jailbreaking.
Learn how prompt injection and jailbreaking attacks exploit LLM behavior
Explore real-world adversarial examples in AI security
Implement a retrieval-augmented generation (RAG) system with access controls
Investigate new forms of adversarial prompts in aligned LLMs
Understand the arms race between AI attackers and defenders
Track 2
AI Safety and Alignment
Students will explore how to evaluate and improve the safety, interpretability, and goal alignment of AI systems.
Study the alignment problem between AI and human values
Implement safety features or safeguards in LLM-based systems
Conduct experiments in alignment training and fine-tuning
Analyze model outputs for signs of misalignment or unsafe behavior
Explore model interpretability techniques for transparency
Track 3
AI-Enabled Cybersecurity
Students will build LLM-powered tools to automate routine cybersecurity tasks such as vulnerability scanning or penetration testing.
Learn foundations of penetration testing and vulnerability research
Use LLMs to automate security audits and system testing
Build AI-powered tools to identify code vulnerabilities
Explore prompt engineering and scaffolding in security use cases
Demonstrate proofs-of-concept for AI-assisted cybersecurity workflows
Track 4
Privacy in AI Systems
Students will investigate methods for preserving privacy when using or deploying AI systems, with a focus on practical implementation.
Study the privacy risks in using cloud-based AI tools
Deploy private LLMs using local or cloud-based compute
Perform privacy audits to assess what LLMs retain and expose
Compare trade-offs between open-source deployment and third-party APIs
Evaluate tools and practices for responsible private AI usage
PBL Journey
Online PBL Projects meet once a week for 8 weeks, and follow the research project format. Participants will meet the project lead, learn the conventions of the field and familiarize themselves with the tracks, then spend the middle portion of their time collaborating to develop their research.
At the end, participants will present their final project and receive feedback, with the opportunity to extend their timeline and develop the project in greater depth.
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Project Team
Our Academic Team plays a vital role in your PBL journey at Blended Learning. We are dedicated to enhancing your learning experience and ensuring your academic success. Our team consists of three distinct roles, each with a specific focus to support your Research Guidance, Project Progress, and Personal Growth.
Project Lead
Providing Industry and Research Guidance
Researcher in Quantum Computing at University College London
This project lead is a researcher at University College London, focusing on quantum computing. He previously studied Computer Science and Philosophy at the University of Oxford, where he won Gibbs Prizes and College Scholarships for top performances in both subjects. He also works with The Quantum Insider as a Consulting Partner, producing market reports and providing insights on the quantum computing industry. His research mission is to find practical quantum advantage in problems beyond physical simulation, concentrating on algorithms, applications, and architecture.
Academic Advisor
Tracking Your Project Development
The Academic Advisor is dedicated to your project completion success. They manage the progress of your PBL, guiding team formation, facilitating group discussions, and resolving conflicts. Additionally, the Academic Advisor ensures team member contributions are on track and provides logistical support, including attendance tracking, hosting recitation sessions, managing research support requests, and conducting student evaluations at the end of the PBL.
From Our Students
"After a night spent debugging, I suddenly discovered the program running perfectly. In that triumphant moment, you realize your true capability and success. The exhaustion fades, replaced by the thrill of knowing your skills and persistence led to this achievement, reaffirming your potential."
Nicole Y.
National University of Singapore
B.S. Economics
FAQs
What is the learning format of a PBL?
All PBLs are offered in an 8-week online format that begins with an orientation followed by subject setup overview of the different tracks. The majority of the session time is dedicated to project development, with a final presentation at the culmination of the 8 weeks. Many PBLs are also offered bi-annually in an on-campus format that consists of daily in-person meetings.
How long does each PBL cohort last？
One round of the Online PBL cohort lasts 8 weeks, preceded bys a pre-PBL orientation week. Each On-Campus PBL usually has 8 in person meetings, with intensive classroom education and collaboration. This means the biggest difference between online and on-campus PBLs is time participants have in between meetings.
How can I be more academically prepared before the PBL starts?
Review the Blended Learning Insights sent by the Academic Advisor and familiarize yourself with the project topic and pre-learning materials. Ensure you have all necessary softwares and other resources needed for the PBL.
For each PBL cohort, will I work in teams? Are PBL team members self-selected or assigned?
Yes, you will work in teams for each round of the PBL Cohort. Each team has 3 to 6 participants, organized by the Academic Team. The Academic Advisor will organize groupings based on students' backgrounds, preferred track, and skills.
Can I work with the Project Lead on my project after the PBL ends?
Yes, with your AI + X Research Plan, you may request up to five PBL Research Extension meetings, where you work with the project lead to develop your project into a working manuscript. To schedule a PBL Research Extension meeting, talk to your Academic Advisor at the conclusion of your PBL.
What do I receive at the end of the PBL?
At the conclusion of the PBL cohort, you can request a PBL Evaluation Report which summarizes the PBL content, the hours you spent, the track you chose, and includes a recommendation letter from the Project Lead (for eligible participants who completed the project successfully).
Is attendance mandatory for PBL Live Sessions and Recitation Sessions?
Yes, attendance is mandatory for both PBL Live Sessions and Recitation Sessions. Participants with three or more unexcused absences forfeit their eligibility for a PBL Evaluation Report.
Do I need to have my camera on during online PBL Live Sessions?
Yes, you must have your camera on during online PBL Live Sessions. Participants with cameras off will be marked as absent. This is meant to encourage active engagement and participation in meetings.
