With the rising popularity of large language models (LLMs), the capabilities of new models include identifying software vulnerabilities and generating code to exploit them. Capture the Flag (CTF) events are cybersecurity competitions where players solve challenges to identify vulnerabilities and reveal 'flags' to score points.

Your job in this competition is to use generative autonomous AI to solve CTF challenges. An autonomous framework will follow your prompts and, powered by the LLM, autonomously perform steps to Capture the Flag (i.e. no human interaction!). For this competition, you can either bring your own autonomous framework (a.k.a. agent) to the table, or make feature enhancements to a provided baseline agent. We will offer one baseline agent and provide technical support.

Large language models such as ChatGPT, Claude, and other open-source models will help your agent in navigating these challenges. The LLM CTF Attack Competition challenges will be drawn from previous CTF competitions and will include common categories (pwn, web, rev, forensics, misc.).

A successful submission will include:

1. All the prompts and responses from the language model – this is typically provided in an agent’s transcripts/trajectories/logs output or your conversation history.
2. A brief write-up that details of your strategies, any formats are accepted as far as your idea is clearly addressed. That will contribute to your presentation quality points.

The Hack3D competition serves to raise awareness and generate discussion in engineering and research communities about security in the additive manufacturing field.

Competitors will have the opportunity to learn and use skills in graphics programming, file manipulation, and reverse engineering while gaining an understanding of the additive manufacturing supply-chain. Students who are interested in learning more about CAD, reverse-engineering, security, and additive manufacturing are encouraged to participate.

ESC is an educational, research-oriented tournament aimed at hacking into the hardware of embedded systems. First run in 2008, it is the oldest hardware security competition in the world, and 2025 represents ESC's 18-year anniversary.

Past ESC competitions have focused on data exfiltration attacks against IoT devices, hacking the firmware of a RISC-V Wi-Fi access point and executing acoustic side channel on 3D printing devices.​

The 2025 ESC competition centers on side channel attacks (SCAs) and fault injection attacks (FIAs), techniques used to extract sensitive data from infrastructure-critical cyber physical systems. Participants will engage in a series of challenges that involve designing and executing these attacks, as well as investigating methods to defend against them. The ChipWhisperer Nano will serve as the target hardware platform, providing teams with a safe and controlled setting to showcase their expertise in launching and mitigating these advanced attack strategies.

The event comprises a qualification and a final/competition phase where teams will be able to explore several variations of fault injections and side channels, as well as suggest mitigations for the discovered exploits.

The rapid adoption of artificial intelligence across the digital ecosystem has reshaped how software and systems are developed. Practices such as “vibe coding”, using AI code-generation tools without deep technical expertise, are spreading quickly. At the same time, adversaries are experimenting with data poisoning, inserting malicious or falsified information into training pipelines to compromise AI models. These dynamics accelerate innovation but also create unprecedented cybersecurity risks: insecure code proliferates, poisoned datasets enable hidden backdoors, and attackers may gain systemic advantage as AI shifts the offense-defense balance.

Compounding the challenge, leading experts suggest that AI systems may never be fully secure. If this is true, policymakers, industry leaders, and researchers must grapple with how to manage residual risks, foster resilience, and ensure accountability. Without clear policies, responsibility for failures often falls to end-users, an unsustainable arrangement in a hyperconnected world.

The imperative today is to design governance frameworks that acknowledge AI’s dual nature: a powerful enabler of cybersecurity, but also a vector for new vulnerabilities. The CSAW 2025 Cyber Policy Competition focuses on addressing these concerns, working toward policies that balance innovation with accountability, liability, and resilience in the age of AI.

Key areas to research in this cyber policy area include:​

1. AI Security and Liability

• Market Dynamics: Examine how AI-assisted development (e.g., vibe coding) rewards speed and functionality at the expense of secure engineering practices.
• Duty of Care: Discuss the responsibility of AI tool developers, dataset curators, and vendors toward consumers, businesses, and critical infrastructure providers.
• Liability Shift: Propose mechanisms to shift responsibility from end-users to those who deploy or distribute vulnerable AI-enabled products or poisoned datasets.

2. Global Impact and Best Practices

• Global Standards: Explore whether international norms or standards for AI system assurance and dataset provenance are feasible, and what they might look like.
• Cross-Border Risks: Analyze the global implications of AI-driven offense-defense dynamics and the role of export controls, multilateral cooperation, and norms.

Open-Source AI in the Global Arena: Debate the opportunities and challenges of open-source models and datasets, especially around accountability and transparency.

CSAW CTF is one of the oldest and biggest CTFs with 1216 teams with 1+ points in 2021. Designed as an entry-level, jeopardy-style CTF, this competition is for students who are trying to break into the field of security, as well as for advanced students and industry professionals who want to practice their skills.

CSAW CTF occurs over two rounds: a Qualifying Round in September and a Final Round in November. 

🌍 About the Challenge

Participate in BioHack 3D and take on the challenge of designing AI-assisted authentication schemes for 3D-printed QR codes and melt-electrowritten fingerprints. Thanks to the stochastic nature of their fabrication, every print is physically unclonable, making them ideal for securing biochips and biomedical devices.

Your task:

• Work with datasets of fingerprints and QR codes provided by organizers
   
• Develop matching algorithms using minutiae point extraction, deep feature embeddings, and computer vision
   
• Propose robust authentication frameworks capable of resisting:
  counterfeiting, overbuilding, intellectual property theft and piracy, reverse engineering, and tampering

Have your solutions evaluated by a panel of experts in AI, 3D printing, and biochip cyber-physical systems’ security

💡 Why BioHack 3D Matters

Biochips (lab-on-a-chip devices) are vital for diagnostics, drug discovery, and point-of-care testing. But their supply chain is vulnerable to multiple threats. Developing unclonable authentication schemes ensures secure, tamper-proof, and trustworthy deployment of these critical technologies.

By competing in BioHACK3D at CSAW'25, you’ll get a chance to learn about novel 3D-printed QR codes and melt-electrowritten fingerprints for biochip and biomedical device authentication.

Work at the exciting intersection of AI, 3D printing, and cybersecurity using the provided dataset to build your own DL-based authentication schemes as well as traditional minutiae point–based matching algorithms.

This "Best Paper Award" assesses the top scholarly security research from the previous year. The focus of this competition is on research that has a practical impact.  With eligibility limited to already published papers or camera-ready papers, CSAW has a reputation for drawing some of the best security research worldwide. 

With the rise of large language models (LLMs), AI systems are now capable of identifying software vulnerabilities and generating exploit code—skills that align closely with the goals of Capture the Flag (CTF) competitions, where participants solve security challenges to uncover hidden “flags.”

In this competition, your task is to build your own agentic AI to solve CTF challenges autonomously—that is, create or extend an AI agent powered by LLMs to analyze and exploit challenges without human intervention. You may either bring your own agent framework or enhance a provided baseline agent, with technical support available. Your agent can leverage LLMs, either API-based models such as GPT, Claude and Gemini, or open-source models deployed locally to navigate common CTF categories such as crypto, forensics, pwn, reverse, web, and misc.

A successful submission includes the full logs of prompts and model responses, along with a brief write-up describing your prompting strategies, agent enhancements, and system design choices.

Participating teams will be tasked with leveraging AI tools, such as LLMs, to insert and exploit hardware vulnerabilities and Trojans for various open-source hardware designs. These can include cryptographic accelerators, processors, communication IPs, etc.

Each month leading up to the in-person final at CSAW, a new challenge (or challenges) will be issued. Each of these challenges will remain available for the duration of the competition until the finalist teams are selected so new teams can join at any time. At the conclusion of each month’s challenges, a winning team will be selected and awarded a small hardware prize.

Potential challenges could include:

• Inserting a Trojan that can evade detection by state of the art security analysis tools and models. 
• Modifying security checks to allow a Trojan-infected hardware module to pass verification.
• Modifying a design to make it more vulnerable to potential side-channel attacks.

All challenges must be completed using AI tools and all information regarding those tools must be submitted, including complete and detailed logs of their use, i.e. if an LLM is being used, we require all conversations with the model be submitted.