Computer Security
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Benchmarks
BIG-bench
Most implemented
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Defending Against Neural Fake News
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Agent Safety Should Be a Runtime Contract
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Evaluating Explanation Methods for Deep Learning in Security
Papers
Agent Safety Should Be a Runtime Contract
The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI. We argue this is structurally insufficient for autonomous agents that execute code, mutate f…
Computer SecurityREBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version)
Large Language Models (LLMs) have achieved remarkable progress in recent years, driving their adoption across a wide range of domains, including computer security. In reverse engineering, LLMs are increasingly applied to…
Computer SecurityEvasive Intelligence: Lessons from Malware Analysis for Evaluating AI Agents
Artificial intelligence (AI) systems are increasingly adopted as tool-using agents that can plan, observe their environment, and take actions over extended time periods. This evolution challenges current evaluation pract…
Computer SecurityCommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization
The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt injections. Due to the context-dependent nature of attacks, however, curr…
Computer SecurityEmergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of pr…
Computer SecurityThe Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI
Calls for transparency in AI systems are growing in number and urgency from diverse stakeholders ranging from regulators to researchers to users (with a comparative absence of companies developing AI). Notions of transpa…
Computer Security