Adversarial Attacks on LLMs

LLM: Large Language Model

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37 papers in the last four weeks, up 311% on the four weeks before. 0.4% of all new papers.

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Market Signal Injection: Adversarial Context Manipulation of LLM Pricing Agents
Sep 16, 2026Dohun Lee, Hyunwoo ParkAdversarial Attacks on LLMs AI Agent Security
Graduate School of Data Science, Seoul National University
Nameless Tokenization: A Lossless Tokenizer-Level Defense Against Control-Token Forgery in Open-Weight LLMs
Sep 15, 2026Kisu Yang, Yoonna Jang, Heuiseok LimAdversarial Attacks on LLMs LLM Security
1VAIV Company · 2Hanwha Aerospace · 3Korea University
Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion
Sep 15, 2026Zhuoang CaiAdversarial Attacks on LLMs LLM Safety Evaluation
The Hong Kong University of Science and Technology
Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration
Sep 14, 2026Aashiq Muhamed, Mona T. Diab, Virginia SmithAdversarial Attacks on LLMs LLM Safety
Carnegie Mellon University
Universal Defenses for Tool-Integrated LLM Agents Against Adversarial Attacks
Sep 14, 2026Xiaoyan Li, Yunli WangAdversarial Attacks on LLMs Backdoor Defense in LLMs
Department of Computer Science University of Toronto Toronto, Ontario, Canada · Digital Technologies Research Centre National Research Council Canada Ottawa, Ontario, Canada
Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
Sep 14, 2026Mark Russinovich, Blake Bullwinkel, Giorgio Severi +2Adversarial Attacks on LLMs Cybersecurity
Microsoft Azure · Microsoft
Overflip: Repetition-Induced Label Flips in Guardrail Models
Sep 14, 2026Xu He, Chih-Hsuan Lin, Hung-Mao Chen +3LLM Guardrails Adversarial Attacks on LLMs
1George Mason University · 2Virginia Tech · 3Missouri University of Science and Technology +1
Arbitrary Cipher Attacks Against Large Language Models Do Not Require Fine-Tuning
Sep 10, 2026Thomas RivasseauAdversarial Attacks on LLMs Jailbreak Attacks
School of Information Studies McGill University
Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning
Sep 9, 2026Jing Guan, Yachao Yang, Zhaoliang Liu +3LLM Fine-Tuning Adversarial Attacks on LLMs
JIUTIAN Research, Beijing, China · Beijing University of Posts and Telecommunications, Beijing, China
CS-Guard: Benchmarking LLM Guardrails for Code Generation Security
Sep 9, 2026Jinyang Li, Mingyu Guo, Hung X. NguyenLLM Guardrails Adversarial Attacks on LLMs
Adelaide University Adelaide, Australia
How Fragile Is Safety Alignment at Frontier Scale? A Single-Direction Attack on a 320B MoE
Sep 9, 2026Yi Shi, Tanyu Chen, Kai ShenMixture-of-Experts Language Models LLM Alignment
Continuum AI
Structural Jailbreaks Generalize but Do Not Compound: A cross-provider and multilingual study of Involuntary In-Context Learning
Sep 8, 2026Tejasvi C. AddagadaLLM Safety Benchmarks Multilingual Language Model Evaluation
Independent researcher
HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving
Sep 8, 2026Han JinLLM Serving Adversarial Attacks on LLMs
Independent Researcher
ACEA: An Adversarial Co-Evolution Arena for Head-to-Head Red-Team and Blue-Team LLM Testing
Sep 8, 2026Yi Ting Shen, Kentaroh Toyoda, Alex LeungLLM Safety Benchmarks Language Model Safety Evaluation
Vulcan Research, AIFT Singapore
Does Deeper Reasoning Compromise Alignment? Revealing and Mitigating of Alignment Collapse in Large Reasoning Models
Sep 8, 2026Yu-Hang Wu, Yu-Jie Xiong, Henghua Zhang +3Adversarial Attacks on LLMs Jailbreak Attacks
Shanghai University of Engineering Science · The Hong Kong University of Science and Technology · A * STAR
Refusal geometry reflects refusal training: diverse refusal prefixes can raise stable rank and weaken refusal vector ablation attacks
Aug 26, 2026Andrey LabunetsAdversarial Attacks on LLMs Adversarial Robustness of Language Models
UC San Diego
Reflex-Guard: A Low-Latency Guardrail for LLM Prompt Safety Using Dense Semantic Embeddings
Aug 18, 2026Istiaque Ahmed, Afia Anjum Borsha, Ranat Das Prangon +2LLM Guardrails Adversarial Attacks on LLMs
Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan · Dept. of Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh · Dept. of Chemical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh +1
Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
Aug 12, 2026Junliang Liu, Ruoyu Li, Wenxin Tang +4Adversarial Attacks on LLMs LLM Agent Security
School of Computer Science and Software Engineering, Shenzhen University · The Chinese University of Hong Kong · The Chinese University of Hong Kong, Shenzhen
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
Aug 12, 2026Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur +1Deception in Language Models Adversarial Attacks on LLMs
University of Illinois Urbana-Champaign
Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models
Aug 11, 2026Shuozhe Cheng, Kunlan Xiang, Mingxuan Li +3Adversarial Attacks on LLMs Speech Language Models
Generating Attacks for LLMs with GFlowNets
Aug 10, 2026Berkay Ozcam, Irem Onen, Mehmet Fatih Amasyali +1Adversarial Prompt Generation Adversarial Attacks on LLMs
Cybersecurity R&D Turkcell Istanbul, Turkey · Department of Computer Engineering Yıldız Technical University Istanbul, Turkey
Stealing Reasoning Traces from Proprietary LLM APIs
Aug 10, 2026Alexander Panfilov, David Schmotz, Ilia Shumailov +5Model Extraction Attacks Adversarial Attacks
MATS Research · ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems +4
Pragmatic Attack Surface: Vulnerabilities of Implicit Context in Large Language Models
Aug 10, 2026Bocheng Chen, Han Zi, Roucheng Ou +5Pragmatic Reasoning in Language Models Adversarial Attacks on LLMs
University of Mississippi · Indiana University Indianapolis · Michigan State University +1
Dual-Adversarial Safety Alignment: Cultivating Intrinsic Threat Comprehension in LRMs
Aug 10, 2026Hongli Shen, Shaopeng Fu, Qinbo Zhang +2Adversarial Training Adversarial Attacks on LLMs
1King Abdullah University of Science and Technology · 2Stony Brook University
When Skills Meet Safety: Benchmarking and Characterizing the Adaptive Jailbreak Robustness of Skill-Merged LLMs
Aug 9, 2026Yu Ma, Hongli Shi, Jing Li +2LLM Safety Benchmarks Language Model Safety Evaluation
Google · University of New South Wales · University of Technology Sydney +2
Forged Peer Judgments Mislead Multimodal LLM Judge Panels: Source-Blind Anchoring and Panel-Consensus Verification
Aug 8, 2026Yang ShuLLM-as-a-Judge Adversarial Attacks on LLMs
Zhejiang University, Hangzhou, China
Diffusion LLMs as Targets and Adversaries: Mechanistic Safety Exploits
Aug 7, 2026Elena Dumitrescu, Gert Lek, Lydia Y. Chen +1Adversarial Attacks on LLMs LLM Safety Alignment
Delft University of Technology, Delft, The Netherlands · University of Neuchâtel, Neuchâtel, Switzerland.
Mood Matters: How Syntactic Sensitivity Undermines Safety Alignment
Aug 5, 2026Alina Klerings, Jannik Brinkmann, Heiner Stuckenschmidt +1Adversarial Attacks on LLMs LLM Safety Alignment
University of Mannheim · Technical University Clausthal
Sharding Prevents LLM Oversight Failures and Adversarial Exploitation
Aug 5, 2026Victor Akinwande, J. Zico Kolter, Aran NayebiScalable Oversight LLM Evaluation
School of Computer Science 2Neuroscience Institute Carnegie Mellon University
Eliciting Intrinsic Hallucinations in LLMs via Semantically Equivalent Adversarial Attacks
Aug 4, 2026Atri Vivek Sharma, Brian Formento, Alessio LomuscioRetrieval-Augmented Generation Adversarial Attacks
Imperial College London · Safe Intelligence

Sep 16, 2026, cs.AIJ/K move · Enter open · S save

Market Signal Injection: Adversarial Context Manipulation of LLM Pricing Agents

Dohun Lee, Hyunwoo Park

Graduate School of Data Science, Seoul National University

Adversarial Attacks on LLMs AI Agent Security Adversarial Robustness of Language Models Multi-Agent LLMs

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Large language model (LLM) pricing agents may respond to how market data is presented, even when its numerical values remain unchanged. We introduce market signal injection (MSI), an attack that manipulates numerical formatting, competitor ordering, or qualitative market commentary without issuing explicit instructions. We evaluate nine open-weight models in simulated Bertrand duopoly and triopoly markets and three proprietary models in duopoly markets. Sentiment-based attacks produce the largest behavioral shifts, which propagate to other firms and alter profits and consumer surplus. Susceptibility varies across model families, and larger models are not consistently more robust. Matched neutral-text controls and a rule-based agent support a framing-based account of these shifts under the fixed demand parameters of our simulation. Episode-held-out probes distinguish baseline from attacked activations in all eleven re-evaluated model--condition pairs: linear AUC is 1.00 and MLP AUC ranges from 0.93 to 0.99. This separability does not by itself identify harmful pricing decisions. Input canonicalization removes the tested sentiment attacks, while decision boundary anchoring, which combines prompt constraints with output projection, provides partial mitigation under the tested adaptive attacks. These results identify data presentation as an attack surface for LLM pricing agents and motivate defenses that account for interactions among agents.

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Latest papers 238