| Vol-3920 urn:nbn:de:0074-3920-X Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# CAMLIS 2024
Conference on Applied Machine Learning in Information Security 2024

### Proceedings of the Conference on Applied Machine Learning in Information Security (CAMLIS 2024)

### Arlington, Virginia, USA, October 24-25, 2024.

Edited by

### Rachel Allen *
Sagar Samtani **
Edward Raff ***
Ethan Rudd****

* Nvidia, 2788 San Tomas Expy, Santa Clara, CA 95051
** Indiana University, 107 S Indiana Ave, Bloomington, IN, USA
*** Booz Allen Hamilton, 304 Sentinel Drive, Annapolis Junction, MD, USA
**** Google, 1600 Amphitheatre Parkway Mountain View, CA 94043

## Table of Contents

- Defending Large Language Models Against Attacks With Residual Stream Activation Analysis 1-15
  Amelia Kawasaki, Andrew Davis, Houssam Abbas
- Curl: Private LLMs through Wavelet-Encoded Look-Up Tables 16-47
  Manuel B. Santos, Dimitris Mouris, Mehmet Ugurbil, Stanislaw Jarecki, José Reis, Shubho Sengupta, Miguel de Vega
- Defending Against Indirect Prompt Injection Attacks With Spotlighting 48-62
  Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, Emre Kıcıman
- Towards autonomous cyber-defence: using co-operative decision making for cybersecurity 63-82
  Madeline Cheah, Jack Stone, Samuel Bailey, Peter Haubrick, David Rimmer, Matt Lacey, Mark Dorn
- PEVuln: A Benchmark Dataset for Using Machine Learning to Detect Vulnerabilities in PE Malware 83-113
  Nathan Ross, Oluwafemi Olukoya, Jesús Martínez del Rincón, Domhnall Carlin
- Applying Action Masking and Curriculum Learning Techniques to Improve Data Efficiency and Overall Performance in Operational Technology Cyber Security using Reinforcement Learning 114-128
  Alec Wilson, William Holmes, Ryan Menzies, Kez Smithson Whitehead
- Navigating Concept Drift and Packing Complexity in Malware Family Classification 129-144
  Numan Halit Guldemir, Oluwafemi Olukoya, Jesús Martínez-del-Rincón
- DIP-ECOD: Improving Anomaly Detection in Multimodal Distributions 145-160
  Kaixi Yang, Paul Miller, Jesús Martinez-del-Rincon
- LLM agents for vulnerability identification and verification of CVEs 161-173
  Tadesse ZeMicheal, Hsin Chen, Shawn Davis, Rachel Allen, Michael Demoret, Ashley Song
- Ransomware Evolution: Unveiling Patterns Using HDBSCAN 174-189
  Prajna Bhandary, Robert J. Joyce, Charles Nicholas
- Is 𝐹1 Score Suboptimal for Cybersecurity Models? Introducing 𝐶𝑠𝑐𝑜𝑟𝑒, a Cost-Aware Alternative for Model Assessment 190-209
  Manish Marwah, Asad Narayanan, Stephan Jou, Martin Arlitt, Maria Pospelova
- AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees 210-223
  William Fleshman, Benjamin Van Durme
- PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI Systems 224-241
  Gary D. Lopez Munoz, Amanda J. Minnich, Roman Lutz, Richard Lundeen, Raja Sekhar Rao Dheekonda, Nina Chikanov, Bolor-Erdene Jagdagdorj, Martin Pouliot, Shiven Chawla, Whitney Maxwell, Blake Bullwinkel, Katherine Pratt, Joris de Gruyter, Charlotte Siska, Pete Bryan, Tori Westerhoff, Chang Kawaguchi, Christian Seifert, Ram Shankar Siva Kumar, Yonatan Zunger
- Binary Malware Attribution using LLM Embeddings and Topological Data Analysis 242-256
  Kincaid MacDonald, Ajai Ruparelia, Boomer Rogers, Adonis Bovell, Branden Stone
- Embedding-based classifiers can detect prompt injection attacks 257-268
  Md. Ahsan Ayub, Subhabrata Majumdar

2025-02-01: submitted by Edward Raff, metadata incl. bibliographic data published under Creative Commons CC0
2025-02-09: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|