arXiv AI
Jun 16
The Proxy Knows Too Much: Sealing LLM API Routers with Attested TEEs
arXiv:2606. 16358v1 Announce Type: cross Abstract: Agents increasingly access large language models (LLMs) through API routers.
Integrity of peer-to-peer distributed LLM inference under malicious nodes
arXiv:2607. 19490v1 Announce Type: cross Abstract: Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes.
Related stories
arXiv Machine Learning
Jul 23
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
arXiv:2603. 07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs).
arXiv Machine Learning
Jul 30
Adaptively Robust LLM Monitoring via Activation Watermarking
arXiv:2603. 23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent.
arXiv Machine Learning
Jun 4
Bypassing Prompt Guards in Production with Controlled-Release Prompting
arXiv:2510. 01529v3 Announce Type: replace Abstract: Ball et al.
arXiv Machine Learning
Aug 11
Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harms
arXiv:2508. 16481v3 Announce Type: replace Abstract: Ensuring the safe use of agentic systems requires a thorough understanding of the range of malicious behaviors these systems may exhibit.
arXiv AI
Aug 11
Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM
arXiv:2511. 18721v4 Announce Type: replace-cross Abstract: The SmoothLLM defense provides a certification guarantee against jailbreaking attacks, but it relies on a strict "k-unstable" assumption that rarely holds in practice.
arXiv AI
Jun 26
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
arXiv:2606. 27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become foundational to modern agent ecosystems.
arXiv Machine Learning
Jun 19
FloatDoor: Platform-Triggered Backdoors in LLMs
arXiv:2606. 19535v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in sensitive settings such as software engineering, where their outputs directly shape downstream artifacts.
arXiv AI
Jul 2
Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
arXiv:2407. 10887v4 Announce Type: replace-cross Abstract: Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse.
arXiv Machine Learning
Jul 7
Walma: Learning to See Memory Corruption in WebAssembly
arXiv:2603. 24167v2 Announce Type: replace-cross Abstract: WebAssembly's (Wasm) monolithic linear memory turns a single memory-corruption bug into a bidirectional threat: a compromised module can attack its embedding host, and a malicious host can tamper with a trusted module's state.
arXiv Machine Learning
Jun 30
FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks
arXiv:2606. 28962v1 Announce Type: cross Abstract: Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Conditioned Backdoor (QCB) attacks.
arXiv AI
Jun 12
Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
arXiv:2606. 13385v1 Announce Type: cross Abstract: Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content and execute actions with direct consequences.