273 citations · 1.1k across the 56 of their papers we have counts for
10 papers · 1 filter
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
Detecting Adversarial Fine-tuning with Auditing Agents
Sarah Egler, John Schulman, Nicholas Carlini
Large Language Model (LLM) providers expose fine-tuning APIs that let end users fine-tune their frontier LLMs. Unfortunately, it has been shown that an adversary with fine-tuning a…
The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
Milad Nasr, Nicholas Carlini, Chawin Sitawarin +11
How should we evaluate the robustness of language model defenses? Current defenses against jailbreaks and prompt injections (which aim to prevent an attacker from eliciting harmful…
Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
Milad Nasr, Yanick Fratantonio, Luca Invernizzi +7
As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level vulnerabilities with real-world…
LLMs unlock new paths to monetizing exploits
Nicholas Carlini, Milad Nasr, Edoardo Debenedetti +5
We argue that Large language models (LLMs) will soon alter the economics of cyberattacks. Instead of attacking the most commonly used software and monetizing exploits by targeting…
In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI
Shayne Longpre, Kevin Klyman, Ruth E. Appel +31
The widespread deployment of general-purpose AI (GPAI) systems introduces significant new risks. Yet the infrastructure, practices, and norms for reporting flaws in GPAI systems re…
AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses
Nicholas Carlini, Javier Rando, Edoardo Debenedetti +2
We introduce AutoAdvExBench, a benchmark to evaluate if large language models (LLMs) can autonomously exploit defenses to adversarial examples. Unlike existing security benchmarks…
Defeating Prompt Injections by Design
Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan +7
Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an untrusted environment. However, LLM agents are vulnerable to prompt injection attack…
Position: Adversarial ML for LLMs Is Not Making Any Progress
Javier Rando, Jie Zhang, Nicholas Carlini +1
In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings. Yet, progress has been slow even fo…
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos +10
It is now common to evaluate Large Language Models (LLMs) by having humans manually vote to evaluate model outputs, in contrast to typical benchmarks that evaluate knowledge or ski…