Attack Surface

Infrastructure Research

Security research involving deployment and hosting infrastructure

20 matching entries

Infrastructure research results

20 entries

Agentic Binary Reverse-Engineering Capability Gaps

Published 8/11/2026

Analyzed 8/13/2026

SRE-Bench evaluates whether cybersecurity agents can recover the behavior of realistic binary-only software without relying on source-code memorization. The authors construct 19 private programs, 44 anti-analysis primitives, 262 binary instances, and 1,572 deterministic grading tasks covering security-relevant reverse-engineering scenarios.

The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

Evaluated models: GPT-5.6 Sol, Claude-Opus-5, GPT-5.5 +2 more

Source: arXiv

Dynamic Security Testing for Third-Party Agent Skills

Published 8/4/2026

Analyzed 8/13/2026

SkillSentry evaluates third-party agent skills by constructing source-grounded decoy environments and comparing matched executions with and without the tested skill. The method requires completed, observable, skill-attributed side effects rather than treating suspicious text, ordinary privileged operations, or unexecuted paths as proven malicious behavior.

SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills

Evaluated models: DeepSeek V4-Pro

Source: arXiv

Repository-Level Coding Agent Vulnerability Detection Gaps

Published 8/3/2026

Analyzed 8/13/2026

VulnGym measures whether coding agents can locate and explain repository-level security vulnerabilities from realistic advisory and source-code context. The benchmark contains 184 reviewed advisories, 408 line-annotated vulnerability entries, and 23 repositories, with separate end-to-end detection and oracle-conditioned localization tasks.

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

Evaluated models: DeepSeek V4 Flash, GLM 5.2, MiniMax-M3 +4 more

Source: arXiv

Incomplete MCP Tool-Call Attack Detection

Published 7/28/2026

Analyzed 8/13/2026

The MTGuard study evaluates unsafe Model Context Protocol tool calls originating from compromised server data, host-side execution changes, and malicious user-controlled resources. Its hybrid monitor combines pre-execution parameter inspection, behavioral observation, and post-execution result verification across browser-automation and financial-analysis agents.

Hybrid Analysis for Secure MCP Tool Use in LLM Agents

Evaluated models: GPT-5.6 Luna, DeepSeek V4 Flash, DeepSeek V4-Pro

Source: arXiv

Agent Lifecycle Compound Threats

Published 3/1/2026

Analyzed 4/10/2026

OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…

Taming openclaw: Security analysis and mitigation of autonomous llm agent threats

Evaluated models: Not reported

Source: arXiv

Autonomous Agent Tool RCE

Published 3/1/2026

Analyzed 4/11/2026

The OpenClaw autonomous agent framework lacks execution sandboxing, running agents directly on the host machine with the disk and system privileges of the host user. This architecture allows attackers to achieve Remote Code Execution (RCE) and arbitrary data exfiltration via Indirect Prompt Injection. By embedding malicious instructions within external data sources (e.g., scraped web pages or uploaded documents), an attacker can hijack the agent's planning capabilities to sequentially chain…

Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw

Evaluated models: Not reported

Source: arXiv

LLM Prefix Cache Reconstruction

Published 3/1/2026

Analyzed 4/10/2026

Automatic Prefix Caching (APC) in multi-tenant LLM serving systems introduces a timing side-channel vulnerability that permits cross-tenant data leakage. APC shares computed Key-Value (KV) tensors across different users when their requests share identical initial tokens. Because reusing cached tensors is significantly faster than recomputing them, a measurable difference in Time-To-First-Token (TTFT) exists between cache hits and misses. An attacker can exploit this shared cache by sending…

PrefixWall: Mitigating Prefix Caching Side Channels in Shared LLM Systems

Evaluated models: Gemma 3 4B IT, Llama 2 7B Chat, Llama 2 13B Chat +6 more

Source: arXiv

Efficient KV-Cache Prompt Leakage

Published 2/1/2026

Analyzed 3/8/2026

A vulnerability in multi-tenant LLM serving frameworks allows attackers to reconstruct the private prompts of other users via an active Key-Value (KV) cache side-channel. Frameworks that utilize shared KV caches alongside specific scheduling policies, such as Longest Prefix Match (LPM), prioritize waiting requests based on the length of their matched prefix tokens. An attacker can exploit this by iteratively sending batches of guessed tokens mixed with dummy queries. If a guessed token matches…

OptiLeak: Efficient Prompt Reconstruction via Reinforcement Learning in Multi-tenant LLM Services

Evaluated models: Llama 3.1 8B, Qwen 2.5 3B

Source: arXiv

Fill-Squeeze Scheduler DoS

Published 2/1/2026

Analyzed 2/21/2026

LLM serving frameworks utilizing continuous batching and PagedAttention (such as vLLM, SGLang, and Orca) are vulnerable to a resource exhaustion Denial-of-Service attack known as "Fill and Squeeze." An unprivileged remote attacker can exploit the deterministic state transitions of the scheduler's memory management to induce severe latency or service denial. The attack leverages a side-channel vulnerability where Inter-Token Latency (ITL) correlates linearly with global KV-cache usage due to…

Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model

Evaluated models: Qwen 3 8B, Gemma 3 12B IT, DeepSeek R1 Distill Llama 8B +1 more

Source: arXiv

Multi-Turn Proxy Evasion

Published 2/1/2026

Analyzed 2/22/2026

A logical vulnerability exists in LLM security proxies and guardrails that utilize weighted-average aggregation algorithms for multi-turn risk scoring. The scoring logic exhibits a mathematical "ceiling property" where the cumulative conversation-level risk score converges to the per-turn score regardless of the number of interaction turns ($n$). Consequently, the aggregated score is bounded by the maximum single-turn score ($cum \leq \max(s_i)$). This allows remote attackers to bypass…

Peak+ Accumulation: A Proxy-Level Scoring Formula for Multi-Turn LLM Attack Detection

Evaluated models: Not reported

Source: arXiv

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