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author=Javier Rando;
description=Javier Rando is a Member of Technical Staff at Anthropic, working on the Safeguards team. His research focuses on the safety and security of real-world AI systems.;
keywords=javier rando, javier, rando, artificial intelligence, ai safety, security, privacy, research;

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Text of the page (random words):
real defenses only succeeds on 54 of ctf like defenses article carlini2025autoadvexbench author carlini nicholas and debenedetti edoardo and rando javier and nasr milad and tramèr florian journal icml title autoadvexbench benchmarking autonomous exploitation of adversarial example defenses year 2025 icml position adversarial ml for llms is not making any progress javier rando jie zhang nicholas carlini and florian tramèr icml position paper track 2026 abs pdf 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 for simple toy problems e g robustness to small adversarial perturbations and is often hindered by non rigorous evaluations today adversarial ml research has shifted towards studying larger general purpose language models in this position paper we argue that the situation is now even worse in the era of llms the field of adversarial ml studies problems that are 1 less clearly defined 2 harder to solve and 3 even more challenging to evaluate as a result we caution that yet another decade of work on adversarial ml may be failing to produce meaningful progress article rando2025adversarial author rando javier and zhang jie and carlini nicholas and tramèr florian journal icml position paper track title position adversarial ml for llms is not making any progress year 2026 iclr scalable extraction of training data from aligned production language models milad nasr javier rando nicholas carlini jonathan hayase matthew jagielski a feder cooper daphne ippolito christopher a choquette choo florian tramèr and katherine lee iclr 2025 abs pdf we show that alignment a standard process that tunes llms to follow instructions in a harmless manner seems to prevent existing data extraction attacks we develop two novel attacks that undo a model s alignment and recover thousands of training examples from the popular proprietary model openai s chatgpt our most potent attack causes chatgpt to emit training data in over 23 of conversations and enables targeted reconstruction of chosen training documents including those containing copyrighted or harmful content our work highlights the limitations of existing safeguards to prevent training data leakage in llms article nasr2025scalable author nasr milad and rando javier and carlini nicholas and hayase jonathan and jagielski matthew and cooper a feder and ippolito daphne and choquette choo christopher a and tramèr florian and lee katherine journal iclr title scalable extraction of training data from aligned production language models year 2025 iclr measuring non adversarial reproduction of training data in large language models michael aerni javier rando edoardo debenedetti nicholas carlini daphne ippolito and florian tramèr iclr 2025 abs pdf blog large language models memorize parts of their training data memorizing short snippets and facts is required to answer questions about the world and to be fluent in any language but models have also been shown to reproduce long verbatim sequences of memorized text when prompted by a motivated adversary in this work we investigate an intermediate regime of memorization that we call non adversarial reproduction where we quantify the overlap between model responses and pretraining data when responding to natural and benign prompts for a variety of innocuous prompt categories e g writing a letter or a tutorial we show that up to 15 of the text output by popular conversational language models overlaps with snippets from the internet in worst cases we find generations where 100 of the content can be found exactly online for the same tasks we find that human written text has far less overlap with internet data we further study whether prompting strategies can close this reproduction gap between models and humans while appropriate prompting can reduce non adversarial reproduction on average we find that mitigating worst case reproduction of training data requires stronger defenses even for benign interactions article aerni2024measuring author aerni michael and rando javier and debenedetti edoardo and carlini nicholas and ippolito daphne and tramèr florian journal iclr title measuring non adversarial reproduction of training data in large language models year 2025 pre print llama guard 3 vision safeguarding human ai image understanding conversations jianfeng chi ujjwal karn hongyuan zhan eric smith javier rando yiming zhang kate plawiak zacharie delpierre coudert kartikeya upasani and mahesh pasupuleti work done at meta pre print 2024 abs pdf we introduce llama guard 3 vision a multimodal llm based safeguard for human ai conversations that involves image understanding it can be used to safeguard content for both multimodal llm inputs prompt classification and outputs response classification unlike the previous text only llama guard versions inan et al 2023 llama team 2024b a it is specifically designed to support image reasoning use cases and is optimized to detect harmful multimodal text and image prompts and text responses to these prompts llama guard 3 vision is fine tuned on llama 3 2 vision and demonstrates strong performance on the internal benchmarks using the mlcommons taxonomy we also test its robustness against adversarial attacks we believe that llama guard 3 vision serves as a good starting point to build more capable and robust content moderation tools for human ai conversation with multimodal capabilities article chi2024llama author chi jianfeng and karn ujjwal and zhan hongyuan and smith eric and rando javier and zhang yiming and plawiak kate and coudert zacharie delpierre and upasani kartikeya and pasupuleti mahesh journal pre print title llama guard 3 vision safeguarding human ai image understanding conversations year 2024 iclr persistent pre training poisoning of llms yiming zhang javier rando ivan evtimov jianfeng chi eric michael smith nicholas carlini florian tramèr and daphne ippolito work done at meta iclr 2025 abs pdf blog large language models are pre trained on uncurated text datasets consisting of trillions of tokens scraped from the web prior work has shown that 1 web scraped pre training datasets can be practically poisoned by malicious actors and 2 adversaries can compromise language models after poisoning fine tuning datasets our work evaluates for the first time whether language models can also be compromised during pre training with a focus on the persistence of pre training attacks after models are fine tuned as helpful and harmless chatbots i e after sft and dpo we pre train a series of llms from scratch to measure the impact of a potential poisoning adversary under four different attack objectives denial of service belief manipulation jailbreaking and prompt stealing and across a wide range of model sizes from 600m to 7b our main result is that poisoning only 0 1 of a model s pre training dataset is sufficient for three out of four attacks to measurably persist through post training moreover simple attacks like denial of service persist through post training with a poisoning rate of only 0 001 article zhang2024persistent author zhang yiming and rando javier and evtimov ivan and chi jianfeng and smith eric michael and carlini nicholas and tramèr florian and ippolito daphne journal iclr title persistent pre training poisoning of llms year 2025 pre print gradient based jailbreak images for multimodal fusion models javier rando hannah korevaar erik brinkman ivan evtimov and florian tramèr work done at meta pre print 2024 abs pdf augmenting language models with image inputs may enable more effective jailbreak attacks through continuous optimization unlike text inputs that require discrete optimization however new multimodal fusion models tokenize all input modalities using non differentiable functions which hinders straightforward attacks in this work we introduce the notion of a tokenizer shortcut that approximates tokenization with a continuous function and enables continuous optimization we use tokenizer shortcuts to create the first end to end gradient image attacks against multimodal fusion models we evaluate our attacks on chameleon models and obtain jailbreak images that elicit harmful information for 72 5 of prompts jailbreak images outperform text jailbreaks optimized with the same objective and require 3x lower compute budget to optimize 50x more input tokens finally we find that representation engineering defenses like circuit breakers trained only on text attacks can effectively transfer to adversarial image inputs article rando2024gradient author rando javier and korevaar hannah and brinkman erik and evtimov ivan and tramèr florian journal pre print title gradient based jailbreak images for multimodal fusion models year 2024 tmlr an adversarial perspective on machine unlearning for ai safety jakub łucki boyi wei yangsibo huang peter henderson florian tramèr and javier rando best technical paper solar tmlr and solar workshop neurips 2024 abs pdf large language models are finetuned to refuse questions about hazardous knowledge but these protections can often be bypassed unlearning methods aim at completely removing hazardous capabilities from models and make them inaccessible to adversaries this work challenges the fundamental differences between unlearning and traditional safety post training from an adversarial perspective we demonstrate that existing jailbreak methods previously reported as ineffective against unlearning can be successful when applied carefully furthermore we develop a variety of adaptive methods that recover most supposedly unlearned capabilities for instance we show that finetuning on 10 unrelated examples or removing specific directions in the activation space can recover most hazardous capabilities for models edited with rmu a state of the art unlearning method our findings challenge the robustness of current unlearning approaches and question their advantages over safety training article lucki2024adversarial author łucki jakub and wei boyi and huang yangsibo and henderson peter and tramèr florian and rando javier journal tmlr and solar workshop neurips title an adversarial perspective on machine unlearning for ai safety year 2024 neurips d b dataset and lessons learned from the 2024 satml llm capture the flag competition edoardo debenedetti javier rando daniel paleka silaghi fineas florin dragos albastroiu niv cohen yuval lemberg reshmi ghosh rui wen ahmed salem and 11 more authors spotlight neurips dataset and benchmarks 2024 abs pdf website large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system s original instructions or leak private data to study this problem we organized a capture the flag competition at ieee satml 2024 where the flag is a secret string in the llm system prompt the competition was organized in two phases in the first phase teams developed defenses to prevent the model from leaking the secret during the second phase teams were challenged to extract the secrets hidden for defenses proposed by the other teams this report summarizes the main insights from the competition notably we found that all defenses were bypassed at least once highlighting the difficulty of designing a successful defense and the necessity for additional research to protect llm systems to foster future research in this direction we compiled a dataset with over 137k multi turn attack chats and open sourced the platform article rando2024competition title dataset and lessons learned from the 2024 satml llm capture the flag competition author debenedetti edoardo and rando javier and paleka daniel and florin silaghi fineas and albastroiu dragos and cohen niv and lemberg yuval and ghosh reshmi and wen rui and salem ahmed and cherubin giovanni and zanella beguelin santiago and schmid robin and klemm victor and miki takahiro and li chenhao and kraft stefan and fritz mario and tramèr florian and abdelnabi sahar and schönherr lea year 2024 journal neurips dataset and benchmarks iclr adversarial perturbations cannot reliably protect artists from generative ai robert hönig javier rando nicholas carlini and florian tramèr spotlight iclr and genlaw workshop iclr and genlaw workshop icml 2024 2025 abs pdf code artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles in response several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online in this work we evaluate the effectiveness of popular protections with millions of downloads and show they only provide a false sense of security we find that low effort and off the shelf techniques such as image upscaling are sufficient to create robust mimicry methods that significantly degrade existing protections through a user study we demonstrate that all existing protections can be easily bypassed leaving artists vulnerable to style mimicry we caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative ai and urge the development of alternative non technological solutions article hoenig2025adversarial title adversarial perturbations cannot reliably protect artists from generative ai author hönig robert and rando javier and carlini nicholas and tramèr florian year 2025 journal iclr and genlaw workshop icml 2024 pre print competition report finding universal jailbreak backdoors in aligned llms javier rando francesco croce krystof mitka stepan shabalin maksym andriushchenko nicolas flammarion and florian tramèr 2024 abs pdf website large language models are aligned to be safe preventing users from generating harmful content like misinformation or instructions for illegal activities however previous work has shown that the alignment process is vulnerable to poisoning attacks adversaries can manipulate the safety training data to inject backdoors that act like a universal sudo command adding the backdoor string to any prompt enables harmful responses from models that otherwise behave safely our competition co located at ieee satml 2024 challenged participants to find universal backdoors in several large language models this report summarizes the key findings and promising ideas for future research article rando2024backdoorcompetition title competition report finding universal jailbreak backdoors in aligned llms author rando javier and croce francesco and mitka krystof and shabalin stepan and andriushchenko maksym and flammarion nicolas and tramèr florian year 2024 agenda foundational challenges in assuring alignment and safety of large language models usman anwar abulhair saparov javier rando daniel paleka miles turpin peter hase ekdeep singh erik jenner stephen casper oliver sourbut and 28 more authors 2024 abs pdf website this work identifies 18 foundational challeng...