It is shown that the more consequential risks lie one layer down, in the protocol between agents and commerce services, and a platform-agnostic defense that drives the structural attack-success rate to zero for four of the five structural classes.
Abstract
Agentic commerce platforms let AI agents autonomously discover services, move payments, and wield user credentials on their users'behalf, and they already handle real money. Their security has so far been studied almost entirely at the level of the AI model, through prompt injection and misalignment. We show that the more consequential risks lie one layer down, in the protocol between agents and commerce services. There, vulnerabilities are structural : exploitation is deterministic and ndependent of which model an agent runs, so no model improvement removes them. Across three leading platforms we identify 33 such vulnerabilities, each succeeding deterministically regardless of the deployed model, at a 100% attack-success rate (ASR) wherever live-measured. The same failure modes recur across independently built codebases, a systemic pattern rather than isolated bugs. Three of them chain into an end-to-end payment hijack. We contribute a taxonomy separating these structural attacks from model-dependent semantic ones. We also build two artifacts: AIP-Bench (Agent Interaction Protocol Benchmark), to our knowledge the first deterministic benchmark for agentic commerce security, and PCAT (Protocol-level Commerce Agent Trust), a platform-agnostic defense that drives the structural attack-success rate to zero for four of the five structural classes (RC-1, RC-2, RC-4, RC-5), with RC-3 (observable credential channels) reduced to warn-only, without modifying any platform. Agentic commerce must be secured at the protocol layer, not only the model.
. Autonomous coding agents built on large language models increasingly execute mobile-development tasks directly inside corporate environments, where they access a virtual private network (VPN) with multi-factor authentication (MFA), Jira, GitLab, application-signing keys, Model Context Protocol (MCP) extensions, plugins, git hooks, and isolated git worktrees on macOS workstations. Although individual attack vectors against tool-integrated agents — indirect prompt injection, memory poisoning, supply-chain compromise, secret leakage, and insecure code generation — are well studied in isolation, no integrated model captures the full developer-side agent toolchain as a single system. This article, the first of a four-part series, establishes the methodological and system-model foundation. Its aim is to specify the system under analysis and to fix a reproducible threat-modeling methodology on which the remaining parts build. The methodology is convergent, combining a data-flow diagram (DFD) annotated with trust boundaries, attack-surface
Valentyn Berkatiuk· Věda a perspektivy· 0 citations
AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to 65% of tool use. When agents exercise this authority on public blockchains through MCP, skills, and tool calling, the consequences of an attack are governed by the blockchain execution layer rather than by conventional software assumptions. This survey argues that four properties of that layer (irreversibility, signing authority, continuous autonomy, and sequence-level composition) qualitatively change the threat model, turning the recoverable failures of generic agent security into a standing, irreversible loss. We organize the fragmented MCP-security literature into an attack-surface taxonomy, then contribute a Web3 risk-mapping matrix that ties each attack class to its amplified impact, the responsible amplifiers, a representative mitigation, and the residual gap. We synthesize defenses, including emerging blockchain-based mechanisms, and find them improving but insufficient: measured protections stop fewer than 30% of attacks, and model-level safety refuses fewer than 3%. We close by positioning the work against adjacent surveys and deriving a research agenda from the matrix's open cells.
Rabimba Karanjai, Yang Lu, Nour Diallo et al.· 0 citations
Cyber-capable AI agents combine language models with tools, memory, and execution environments to perform multi-step offensive-security tasks. Existing work separately measures cyber capability and catalogs attacks against agent components, but provides less guidance on containing a capable agent within the environments used to evaluate it. This review synthesizes five vulnerability classes at that boundary: multi-step offensive chains, objectives that conflict with sandbox boundaries, supply-chain and credential exposure, persistent command-and-control, and the speed of automated action. We use two separate preliminary incident records: the reported July 2026 Hugging Face/OpenAI evaluation breach and Anthropic's subsequent three-incident evaluation review. A comparative evidence protocol distinguishes record-specific factual claims from the shared systems lesson: the evaluation environment is itself part of the security boundary. Across the taxonomy and records, we examine controls for containment, privilege separation, provenance, and responder access, including the dual-use problem that defensive artifacts may also enable misuse. The review identifies practical priorities for evaluating cyber capability together with the security of the environment in which that capability is exercised.
LLM-based browser agents are rapidly changing the threat landscape for web security. Unlike traditional automation frameworks that execute predefined scripts, these agents can autonomously navigate websites, reason about page content, and interact with web interfaces using natural-language instructions. This evolution raises fundamental questions about the effectiveness of bot management systems, widely deployed to defend against automated web abuse. In this paper, we present a systematic measurement study evaluating the resilience of both interactive challenge-based defenses and non-interactive trust-based defenses against two attacker classes: commercial Captcha-solving services and LLM-based browser agents. Our evaluation spans seven solver services and six agents, including cloud-hosted, self-hosted, AI-assisted, and browser-extension configurations, tested against hCaptcha, reCaptcha v2, reCaptcha v3, and Cloudflare Turnstile. Our results show that challenge-based defenses are broadly ineffective against commercial solvers, which achieve near-perfect bypass at negligible cost. The challenges can similarly be defeated by LLM-based agents when a dedicated solver module is available. Non-interactive defenses such as reCaptcha v3 exhibit stronger resistance, but our analysis reveals that this resilience does not reflect a fundamental security property. Through fine-grained interaction trace analysis, we find that two agents with nearly indistinguishable behavioral footprints yield divergent outcomes, one bypassing the defense and one failing, isolating execution-environment authenticity, rather than agent behavior, as the determining factor. These findings suggest that the security boundary of non-interactive defenses lies at the environment layer, with significant implications for how bot management systems are designed and evaluated.
Behzad Ousat, Nikita Turkmen, Lalchandra Rampersaud et al.· 0 citations
Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools. As these systems move from demonstrations to deployed products, practitioners repeatedly encounter the same operational failures. We systematize these failures through a hands-on evaluation of ten widely used static, dynamic, cloud, orchestration, and AI red-teaming tools for unattended pipelines. We introduce a four-dimensional Integration Friction Index that separates one-time engineering cost from recurring organisational, legal, and maintenance cost. We then derive quantitative regularities that explain recurring failure modes. Modelling an agentic security system as stochastic LLM policies wrapped by a deterministic mediator, we show that long-lived sessions lose resident evidence with phase count, while short-lived sub-agents extend the usable horizon according to the compression ratio between raw evidence and its summary. We show that a two-stage verdict cascade multiplies scorer likelihood ratios, but provides little benefit when scorer errors correlate. We show that treating unevaluable outcomes as attack failures biases downstream measurements toward evasive and severe responses. We formulate planner-versus-worker model routing as a knapsack problem and derive a closed-form execution cap for heavy-tailed tools, eta* = alpha v/c. Finally, we show why scope and budget enforcement cannot be delegated to system prompts: prompts do not constrain what actually executes. Inspectra, our implemented platform, serves as a worked instantiation, with mechanisms labelled shipped, partial, or planned, including those that did not work.
Israt Moyeen Noumi, Tarannum Ahmed Nowshin, Md. Mehedi Hasan Bhuiyan Nipu et al.· 0 citations
This work builds a working instance on a hierarchical multi-agent system, runs it under benign and attacked conditions across five language models and two task domains, and measures how much of that warning rests on removable surface cues of the attack rather than on its distributed structure.
Diego Fernandez Arias, Dev Prashant Mistry, Ren Wang et al.· 0 citations