近期研究探索增强大型语言模型（LLM）代理的可靠性和安全性。一项研究引入了DiagChain，一个用于评估LLM代理在网络安全攻击链重建方面的基准，揭示即使是先进模型在证据整合和排序方面也存在困难。另一篇论文MAFIA展示了一个仅查询的攻击框架，该框架利用LLM代理中的内存模块，在逃避检测的同时取得了高成功率。为了对抗这些漏洞，正在开发新方法，包括一个经过验证的工具调用包装器，它提高了在非原子故障下的可靠性，以及一个名为$S^3$的多阶段防御框架，该框架使用特定阶段的安全技能来减轻代理工作流程中的风险。此外，SIEVE提供了一种选择性验证策略，通过优先进行确定性检查并将模糊案例升级来防御间接提示注入。最后，对LLM代理安全理论基础的研究表明，攻击分布熵而非模型架构决定了形式化监控器的有效性，这为选择和设计安全机制提供了新方法。
AI
arXiv:2608.07430v1 Announce Type: cross Abstract: Diffusion Large Language Models (DLLMs) replace autoregressive next-token prediction with iterative parallel denoising, yet their internal safety mechanisms remain poorly understood. In this work, we investigate DLLMs both as targ…
arXiv cs.AI
TIER_1English(EN)·Wenhao Lin, Chenyu Yu, Xingwei Lin, Sicong Cao, Xiang Chen, Lei Xue, Le Yu, Letian Sha, Chunming Wu·
arXiv:2608.05695v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly invoke external tools and interact with real-world systems, unsafe actions may cause irreversible consequences on external states, user data, and downstream services. Recent runtime …
arXiv:2608.05199v1 Announce Type: cross Abstract: Autonomous security agents operate as staged pipelines, such as classifying network traffic and then attributing attacks to a specific technique. Split conformal prediction gives each stage finite-sample coverage, but deployment r…
arXiv cs.AI
TIER_1English(EN)·Zibo Xiao, Haoyu Wang, Jun Sun·
arXiv:2608.02683v1 Announce Type: cross Abstract: Large Language Model (LLM) agents rely on multi-stage agentic workflows, with stages such as memory, planning, and tool execution, to accomplish complex tasks. However, risks may emerge at different stages, propagate across steps,…
arXiv:2608.03844v1 Announce Type: new Abstract: Memory-augmented LLM agents rely on rich context for long-horizon reasoning and acting, yet their memory modules expose a persistent attack surface for malicious records, making the study of memory poisoning threats imperative. Howe…
arXiv:2608.02645v1 Announce Type: cross Abstract: Large Language Model (LLM) agents rely on external tools to perform multistage tasks. Existing agent frameworks typically assume that tool calls are atomic and return binary success or failure signals. However, real-world systems …
arXiv:2608.03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions. However, existing benchmarks mainly evaluate fin…
arXiv:2512.06716v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used as the core of agentic systems due to their strong reasoning, planning, and tool-use capabilities. By interacting with external environments, LLM agents can execute real-w…
arXiv:2608.02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent its…
arXiv:2608.01388v1 Announce Type: cross Abstract: Runtime safety monitors based on Linear Temporal Logic (LTL) and finite automata (FSA) are increasingly deployed to intercept unsafe tool-call sequences in LLM agents. Yet the same monitor achieves 68-75% attack coverage on some m…
dev.to — LLM tag
TIER_1English(EN)·kirandeepjassal-crypto·
<p><em>Originally published at <a href="https://prepstack.co.in/blog/enterprise-ai-security-missing-layer" rel="noopener noreferrer">prepstack.co.in</a></em></p> <blockquote> <p>Everyone is shipping AI features. Almost nobody is shipping AI <em>security</em>. The model gets a cod…
dev.to — LLM tag
TIER_1English(EN)·Nikhil raman K·
<p>"The biggest security risk in enterprise AI may not be the model serving millions of users—it may be the evaluation pipeline used by only a handful of engineers."</p> <p>For the past two years, most discussions around AI security have focused on prompt injection, RAG vulnerabi…
<blockquote> <p><em>This post was created with AI assistance and reviewed for accuracy before publishing.</em></p> </blockquote> <p>Hackers love prompt injection. It is the easiest way to break an AI app. They paste instructions like "ignore previous steps" into your search box. …
