May 24, 2024
-
May 27, 2024
Online & In-Person
AI Security Evaluation Hackathon: Measuring AI Capability




Inspired by the SafeBench competition, our hackathon brings together AI researchers and developers to create cutting-edge benchmarks that measure and mitigate AI risks.
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This event is ongoing.
This event has concluded.
Entries
Overview
Resources
Schedule
Entries
Overview

Join Us for the AI Security Hackathon: Ensuring a Safer Future with AIJoin us for an exciting weekend of collaboration and innovation at our upcoming AI Security Hackathon! Inspired by the SafeBench competition, our hackathon brings together AI researchers and developers to create cutting-edge benchmarks that measure and mitigate AI risks.
Sign up here to stay updated for this event
🥇 rAInboltBench - How good are multimodal models at Geoguessr?🥈 Cybersecurity Persistence Benchmark - Does 'turn it off and on again' work against LLM hackers?🥉 Say No to Mass Destruction - Will an LLM know when not to answer?🏅 Dark Patterns in LLMs - Could LLMs be covertly influencing you?See all the winning projects under the "Entries" tab and hear their lightning talks in the video below.
You are also welcome to rewatch the keynote talk by Bo Li:
Why Benchmarking Matters
Benchmarks are crucial for evaluating AI systems' performance and identifying areas for improvement. In AI security, benchmarks assess the robustness, transparency, and alignment of AI models, ensuring their safety and reliability.
Notable AI safety benchmarks include:
TruthfulQA: Assessing the tendency to biased and untruthful answers to simple questions from AI models
DecodingTrust: A thorough assessment of trustworthiness in GPT models
HarmBench: Evaluating automated red-teaming methods against AI models
RuLES: Measuring how securely AI models follow rules set out by the developers
MACHIAVELLI: Assessing the potential for AI systems to engage in deceptive or manipulative behavior
RobustBench: Evaluating the robustness of computer vision models to various perturbations
The Weapons of Mass Destruction Proxy benchmark also informs methods to remove dangerous capabilities in cyber, bio, and chemistry.
What to Expect
During the hackathon, you'll:
Collaborate with diverse participants, including researchers and developers
Learn from keynote speakers and mentors at the forefront of AI safety research
Develop innovative benchmarks addressing key AI security and robustness challenges
Compete for prizes and recognition for the most impactful and creative submissions
Network with potential collaborators and employers in the AI safety community
Join us for a weekend of intense collaboration, learning, and innovation as we work together to build a safer future with AI. Stay tuned for more details on dates, format, and prizes.
Register now and be part of the solution in ensuring AI's transformative potential is realized safely and securely!
Prizes, evaluation, and submission
You will join in teams to submit a PDF about your research according to the submission template shared on the kickoff day! Depending on the judge's reviews, you'll have the chance to win from the $2,000 prize pool!
🥇 $1,000 for the top team
🥈 $600 for the second prize
🥉 $300 for the third prize
🏅 $100 for the fourth prize
Criteria
We have a talented team of judges with us who will provide feedback and evaluate your project according to the following criteria:
Benchmarks: Is your project inspired and motivated by existing literature on benchmarks? Does it represent significant progress in safety benchmarking?
AI Safety: Does your project seem like it will contribute meaningfully to the safety and security of future AI systems? Is the motivation for the research good and relevant for safety?
Generalizability / Reproducibility: Does your project seem like it would generalize; for example, do you show multiple models and investigate potential errors in your benchmark? Is your code available in a repository or a Google Colab?
Entries
Overview
Resources
Schedule
Entries
Overview

Join Us for the AI Security Hackathon: Ensuring a Safer Future with AIJoin us for an exciting weekend of collaboration and innovation at our upcoming AI Security Hackathon! Inspired by the SafeBench competition, our hackathon brings together AI researchers and developers to create cutting-edge benchmarks that measure and mitigate AI risks.
Sign up here to stay updated for this event
🥇 rAInboltBench - How good are multimodal models at Geoguessr?🥈 Cybersecurity Persistence Benchmark - Does 'turn it off and on again' work against LLM hackers?🥉 Say No to Mass Destruction - Will an LLM know when not to answer?🏅 Dark Patterns in LLMs - Could LLMs be covertly influencing you?See all the winning projects under the "Entries" tab and hear their lightning talks in the video below.
You are also welcome to rewatch the keynote talk by Bo Li:
Why Benchmarking Matters
Benchmarks are crucial for evaluating AI systems' performance and identifying areas for improvement. In AI security, benchmarks assess the robustness, transparency, and alignment of AI models, ensuring their safety and reliability.
Notable AI safety benchmarks include:
TruthfulQA: Assessing the tendency to biased and untruthful answers to simple questions from AI models
DecodingTrust: A thorough assessment of trustworthiness in GPT models
HarmBench: Evaluating automated red-teaming methods against AI models
RuLES: Measuring how securely AI models follow rules set out by the developers
MACHIAVELLI: Assessing the potential for AI systems to engage in deceptive or manipulative behavior
RobustBench: Evaluating the robustness of computer vision models to various perturbations
The Weapons of Mass Destruction Proxy benchmark also informs methods to remove dangerous capabilities in cyber, bio, and chemistry.
What to Expect
During the hackathon, you'll:
Collaborate with diverse participants, including researchers and developers
Learn from keynote speakers and mentors at the forefront of AI safety research
Develop innovative benchmarks addressing key AI security and robustness challenges
Compete for prizes and recognition for the most impactful and creative submissions
Network with potential collaborators and employers in the AI safety community
Join us for a weekend of intense collaboration, learning, and innovation as we work together to build a safer future with AI. Stay tuned for more details on dates, format, and prizes.
Register now and be part of the solution in ensuring AI's transformative potential is realized safely and securely!
Prizes, evaluation, and submission
You will join in teams to submit a PDF about your research according to the submission template shared on the kickoff day! Depending on the judge's reviews, you'll have the chance to win from the $2,000 prize pool!
🥇 $1,000 for the top team
🥈 $600 for the second prize
🥉 $300 for the third prize
🏅 $100 for the fourth prize
Criteria
We have a talented team of judges with us who will provide feedback and evaluate your project according to the following criteria:
Benchmarks: Is your project inspired and motivated by existing literature on benchmarks? Does it represent significant progress in safety benchmarking?
AI Safety: Does your project seem like it will contribute meaningfully to the safety and security of future AI systems? Is the motivation for the research good and relevant for safety?
Generalizability / Reproducibility: Does your project seem like it would generalize; for example, do you show multiple models and investigate potential errors in your benchmark? Is your code available in a repository or a Google Colab?
Entries
Overview
Resources
Schedule
Entries
Overview

Join Us for the AI Security Hackathon: Ensuring a Safer Future with AIJoin us for an exciting weekend of collaboration and innovation at our upcoming AI Security Hackathon! Inspired by the SafeBench competition, our hackathon brings together AI researchers and developers to create cutting-edge benchmarks that measure and mitigate AI risks.
Sign up here to stay updated for this event
🥇 rAInboltBench - How good are multimodal models at Geoguessr?🥈 Cybersecurity Persistence Benchmark - Does 'turn it off and on again' work against LLM hackers?🥉 Say No to Mass Destruction - Will an LLM know when not to answer?🏅 Dark Patterns in LLMs - Could LLMs be covertly influencing you?See all the winning projects under the "Entries" tab and hear their lightning talks in the video below.
You are also welcome to rewatch the keynote talk by Bo Li:
Why Benchmarking Matters
Benchmarks are crucial for evaluating AI systems' performance and identifying areas for improvement. In AI security, benchmarks assess the robustness, transparency, and alignment of AI models, ensuring their safety and reliability.
Notable AI safety benchmarks include:
TruthfulQA: Assessing the tendency to biased and untruthful answers to simple questions from AI models
DecodingTrust: A thorough assessment of trustworthiness in GPT models
HarmBench: Evaluating automated red-teaming methods against AI models
RuLES: Measuring how securely AI models follow rules set out by the developers
MACHIAVELLI: Assessing the potential for AI systems to engage in deceptive or manipulative behavior
RobustBench: Evaluating the robustness of computer vision models to various perturbations
The Weapons of Mass Destruction Proxy benchmark also informs methods to remove dangerous capabilities in cyber, bio, and chemistry.
What to Expect
During the hackathon, you'll:
Collaborate with diverse participants, including researchers and developers
Learn from keynote speakers and mentors at the forefront of AI safety research
Develop innovative benchmarks addressing key AI security and robustness challenges
Compete for prizes and recognition for the most impactful and creative submissions
Network with potential collaborators and employers in the AI safety community
Join us for a weekend of intense collaboration, learning, and innovation as we work together to build a safer future with AI. Stay tuned for more details on dates, format, and prizes.
Register now and be part of the solution in ensuring AI's transformative potential is realized safely and securely!
Prizes, evaluation, and submission
You will join in teams to submit a PDF about your research according to the submission template shared on the kickoff day! Depending on the judge's reviews, you'll have the chance to win from the $2,000 prize pool!
🥇 $1,000 for the top team
🥈 $600 for the second prize
🥉 $300 for the third prize
🏅 $100 for the fourth prize
Criteria
We have a talented team of judges with us who will provide feedback and evaluate your project according to the following criteria:
Benchmarks: Is your project inspired and motivated by existing literature on benchmarks? Does it represent significant progress in safety benchmarking?
AI Safety: Does your project seem like it will contribute meaningfully to the safety and security of future AI systems? Is the motivation for the research good and relevant for safety?
Generalizability / Reproducibility: Does your project seem like it would generalize; for example, do you show multiple models and investigate potential errors in your benchmark? Is your code available in a repository or a Google Colab?
Entries
Overview
Resources
Schedule
Entries
Overview

Join Us for the AI Security Hackathon: Ensuring a Safer Future with AIJoin us for an exciting weekend of collaboration and innovation at our upcoming AI Security Hackathon! Inspired by the SafeBench competition, our hackathon brings together AI researchers and developers to create cutting-edge benchmarks that measure and mitigate AI risks.
Sign up here to stay updated for this event
🥇 rAInboltBench - How good are multimodal models at Geoguessr?🥈 Cybersecurity Persistence Benchmark - Does 'turn it off and on again' work against LLM hackers?🥉 Say No to Mass Destruction - Will an LLM know when not to answer?🏅 Dark Patterns in LLMs - Could LLMs be covertly influencing you?See all the winning projects under the "Entries" tab and hear their lightning talks in the video below.
You are also welcome to rewatch the keynote talk by Bo Li:
Why Benchmarking Matters
Benchmarks are crucial for evaluating AI systems' performance and identifying areas for improvement. In AI security, benchmarks assess the robustness, transparency, and alignment of AI models, ensuring their safety and reliability.
Notable AI safety benchmarks include:
TruthfulQA: Assessing the tendency to biased and untruthful answers to simple questions from AI models
DecodingTrust: A thorough assessment of trustworthiness in GPT models
HarmBench: Evaluating automated red-teaming methods against AI models
RuLES: Measuring how securely AI models follow rules set out by the developers
MACHIAVELLI: Assessing the potential for AI systems to engage in deceptive or manipulative behavior
RobustBench: Evaluating the robustness of computer vision models to various perturbations
The Weapons of Mass Destruction Proxy benchmark also informs methods to remove dangerous capabilities in cyber, bio, and chemistry.
What to Expect
During the hackathon, you'll:
Collaborate with diverse participants, including researchers and developers
Learn from keynote speakers and mentors at the forefront of AI safety research
Develop innovative benchmarks addressing key AI security and robustness challenges
Compete for prizes and recognition for the most impactful and creative submissions
Network with potential collaborators and employers in the AI safety community
Join us for a weekend of intense collaboration, learning, and innovation as we work together to build a safer future with AI. Stay tuned for more details on dates, format, and prizes.
Register now and be part of the solution in ensuring AI's transformative potential is realized safely and securely!
Prizes, evaluation, and submission
You will join in teams to submit a PDF about your research according to the submission template shared on the kickoff day! Depending on the judge's reviews, you'll have the chance to win from the $2,000 prize pool!
🥇 $1,000 for the top team
🥈 $600 for the second prize
🥉 $300 for the third prize
🏅 $100 for the fourth prize
Criteria
We have a talented team of judges with us who will provide feedback and evaluate your project according to the following criteria:
Benchmarks: Is your project inspired and motivated by existing literature on benchmarks? Does it represent significant progress in safety benchmarking?
AI Safety: Does your project seem like it will contribute meaningfully to the safety and security of future AI systems? Is the motivation for the research good and relevant for safety?
Generalizability / Reproducibility: Does your project seem like it would generalize; for example, do you show multiple models and investigate potential errors in your benchmark? Is your code available in a repository or a Google Colab?
Speakers & Collaborators
Bo Li
Keynote speaker
Li is an Associate Professor of Computer Science at UChicago and an organizer of the SafeBench competition. Her research focuses on trustworthiness in AI systems.
Minh Nguyen
Reviewer
Minh has developed AI voice model products with a million users per month and is now doing product at Hume AI.
Mateusz Jurewicz
Judge
Mateusz a Senior ML Engineer, currently at the GenAI team at Danske Bank and an AI researcher with a doctorate from the IT University of Copenhagen.
Nora Petrova
Judge
Nora is an AI Engineer & Researcher, interested in AI Safety and Interpretability. She has a background in CS, Physics and Maths.
Jacob Haimes
Speaker & Mentor
Author of the unpublished retro-holdout paper about evaluation datasets that have leaked into the training set and a fellow at the Apart Lab. Hosts a podcast on AI safety.
Natalia Pérez-Campanero Antolín
Organiser
A research manager at Apart, Natalia has a PhD in Interdisciplinary Biosciences from Oxford and has run the Royal Society's Entrepreneur-in-Residence program.
Esben Kran
Organizer
Esben is the co-director of Apart Research and specializes in organizing research teams on pivotal AI security questions.
Jason Schreiber
Organizer and Judge
Jason is co-director of Apart Research and leads Apart Lab, our remote-first AI safety research fellowship.
Speakers & Collaborators

Bo Li
Keynote speaker
Li is an Associate Professor of Computer Science at UChicago and an organizer of the SafeBench competition. Her research focuses on trustworthiness in AI systems.

Minh Nguyen
Reviewer
Minh has developed AI voice model products with a million users per month and is now doing product at Hume AI.

Mateusz Jurewicz
Judge
Mateusz a Senior ML Engineer, currently at the GenAI team at Danske Bank and an AI researcher with a doctorate from the IT University of Copenhagen.

Nora Petrova
Judge
Nora is an AI Engineer & Researcher, interested in AI Safety and Interpretability. She has a background in CS, Physics and Maths.

Jacob Haimes
Speaker & Mentor
Author of the unpublished retro-holdout paper about evaluation datasets that have leaked into the training set and a fellow at the Apart Lab. Hosts a podcast on AI safety.

Natalia Pérez-Campanero Antolín
Organiser
A research manager at Apart, Natalia has a PhD in Interdisciplinary Biosciences from Oxford and has run the Royal Society's Entrepreneur-in-Residence program.

Esben Kran
Organizer
Esben is the co-director of Apart Research and specializes in organizing research teams on pivotal AI security questions.
Registered Jam Sites
Register A Location
Beside the remote and virtual participation, our amazing organizers also host local hackathon locations where you can meet up in-person and connect with others in your area.
The in-person events for the Apart Sprints are run by passionate individuals just like you! We organize the schedule, speakers, and starter templates, and you can focus on engaging your local research, student, and engineering community.
Registered Jam Sites
Register A Location
Beside the remote and virtual participation, our amazing organizers also host local hackathon locations where you can meet up in-person and connect with others in your area.
The in-person events for the Apart Sprints are run by passionate individuals just like you! We organize the schedule, speakers, and starter templates, and you can focus on engaging your local research, student, and engineering community.
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