Attack-Surface

6 reports

All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Deep Signal Intelligence Report Security Operations

AI Agent Hijacking via Legacy Infrastructure Exploits

ATLAS OWASP HIGH ▲ 7.5 The Hacker News Jun 23, 2026

Attackers are bypassing AI-layer defences entirely by exploiting unpatched legacy infrastructure — misconfigured Active Directory, stale credentials, and over-privileged IAM roles — to hijack the resources AI agents depend on. Research cited in the article shows 70% of organisations grant AI systems more access than a human in the same role, driving a 76% incident rate among over-privileged deployments. The article argues that securing AI agents requires closing the underlying infrastructure exposure gap, not just hardening the model layer.

Excessive Agency in AI Agents Enables Enterprise Breaches

ATLAS OWASP HIGH ▲ 7.2 Dark Reading Jun 04, 2026

Enterprises deploying AI agents with elevated permissions and minimal oversight face compounding security risks as agentic systems gain the ability to take real-world actions with limited human intervention. The attack surface expands dramatically when agents can access APIs, execute code, and chain decisions autonomously, making containment of a compromise significantly harder. Security teams must implement least-privilege principles and robust monitoring before agentic deployments scale beyond their ability to govern.

Adversa AI: 89% of AI Agents Fail Security Tests

ATLAS OWASP HIGH ▲ 8.2 SecurityWeek Jun 04, 2026

Adversa AI's AI Risk Quadrant report evaluated 100 AI agents across ten categories, finding that only 11 qualify as both capable and well-defended. The research identifies a structural 'power-protection inversion' where the most capable agents also present the widest attack surface, driven by a 'lethal trifecta' of private data access, exposure to untrusted content, and outbound action capability. Computer and coding agents showed the most severe exposure, raising urgent concerns about autonomous agent deployment in enterprise environments.

AI Agents Weaponise Vulnerability Discovery as AI-Generated Code Expands Attack Surface

ATLAS OWASP HIGH ▲ 7.5 Dark Reading May 17, 2026

AI agents are now capable of autonomously discovering and exploiting obscure software vulnerabilities, raising the stakes for defenders already struggling with the volume of potentially insecure AI-generated code flooding codebases. The convergence of agentic exploitation capabilities and mass AI-assisted development creates a compounding risk: more vulnerabilities introduced at scale, and more capable automated systems to find and abuse them. Security teams must adapt their tooling, processes, and threat models to account for both sides of this AI-driven equation.

Flowise and n8n: Auth Bypass in Exposed LLM Services

ATLAS OWASP HIGH ▲ 8.5 The Hacker News May 06, 2026

A scan of over one million exposed AI services found pervasive security failures including absent authentication, leaked API keys, and exposed business logic across self-hosted LLM deployments. Agent management platforms such as Flowise and n8n were discovered internet-exposed without access controls, revealing credential lists and internal workflows. The findings indicate systemic misconfiguration risk as enterprises race to self-host AI infrastructure without applying baseline security practices.

agent-desktop Prompt Injection Grants AI Agents OS Control

ATLAS OWASP HIGH ▲ 7.2 HN AI Security May 03, 2026

agent-desktop is an open-source Rust CLI tool that exposes full OS accessibility trees to AI agents, enabling programmatic control of any desktop application without screenshots or browser sandboxing. This dramatically expands the attack surface for agentic AI systems, as a compromised or prompt-injected agent could silently manipulate native applications, exfiltrate data, or perform destructive actions across the host OS. The tool's deterministic element references and structured JSON output make it trivially scriptable, lowering the barrier for AI-driven desktop abuse.