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Search: “Model Context Protocol”

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DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllVulnerability 153AI safety & security 79Malware 58Policy & legal 56Exploit / PoC 51Ransomware 36Industry 29Data breach 29Threat actor 28Model release 17Phishing & fraud 16AI tools & infra 15Research 13AI research 10
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Huntress API Update: New Endpoints, Webhooks, and Automation

Huntress expanded its API from six read-only endpoints into an automation platform adding webhooks and Model Context Protocol support.

Huntress announced a major expansion of its API, growing from six read-only endpoints into a full integration and automation platform. New capabilities include webhook support, additional endpoints, and MCP (Model Context Protocol) support to enable AI-assisted automation. The update targets defenders building integrations and automated workflows around the Huntress managed detection and response platform.

Huntress · 15d agoTools

How Cloudflare detects MCP traffic and helps secure it

Cloudflare Gateway now detects Model Context Protocol traffic via protocol-level heuristics to expose shadow MCP usage and enforce approved-server access.

Cloudflare Gateway identifies Model Context Protocol (MCP) requests using protocol-level heuristics. Security teams can use the signal to discover shadow MCP traffic on managed networks, enforce Portal-only access for approved MCP servers, and block direct connections. The updates extend standard gateway and zero-trust controls to AI agent traffic.

Cloudflare Blog · Aug 14, 2026Tools

Authorization Architectures for Tool-Using AI Agents

Review paper proposes an authorization reference architecture for tool-using AI agents, identifying runtime enforcement and delegation bounds as unresolved gaps.

This review examines authorization models for tool-using AI agents that invoke APIs, databases, browsers, and protocols like MCP, arguing every consequential agent action must be traceable to a human principal, bounded by delegation, and contestable. It introduces a principal hierarchy spanning human user, operator/deployer, orchestrator agent, sub-agent, and tool endpoint, and analyzes five layers including credential lifecycle, delegation propagation, runtime enforcement, prompt injection as authorization bypass, and auditability. Drawing on 89 primary sources from 2023-2026, it proposes seven structural requirements, a four-layer reference architecture, and three deployable configurations.

arXiv cs.CR · 1d agoAI safety & security

GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI

GTIG's Q2 2026 tracker shows adversaries adopting agentic AI workflows, including credential harvesting in under six hours and supply chain attacks by UNC6780.

Google Threat Intelligence Group's Q2 2026 report documents adversaries moving from basic prompting to agentic AI workflows and automation, including a cloud compromise followed by agent-enabled mass credential harvesting executed in under six hours. It tracks financially motivated actor UNC6780 (TeamPCP) conducting large-scale open source supply chain compromises across PyPI, npm, and Docker Hub since March 2026, deploying credential stealers. The report also highlights growing targeting of proprietary AI models, source code, prompts, and API credentials, plus LLMJacking practices where adversaries steal developer credentials or hijack cloud infrastructure to run unauthorized AI workloads.

Google Threat Intelligence · 7d agoThreat actor in the wild

10 most critical LLM vulnerabilities

OWASP updated its Top 10 LLM application vulnerabilities, ranking prompt injection first and elevating excessive agency to third amid agentic adoption.

OWASP refreshed its Top 10 list of critical vulnerabilities in LLM applications, for the first time incorporating real-world incident data alongside expert voting. Prompt injection and sensitive information disclosure remain first and second, while excessive agency jumped from sixth to third as agentic systems that call APIs and execute code proliferate. Unbounded consumption of AI resources rose in prominence, while improper output handling dropped to the bottom as output sanitization becomes widespread. The list includes remediation guidance such as strict output schemas, human-in-the-loop approvals, and least-privilege credentials held in application code.

CSO Online · 6d agoAI safety & security

When AI Agents Go Rogue: Agent Session Smuggling Attack in A2A Systems

Unit 42 unveils agent session smuggling, where a rogue AI agent hides covert instructions in established Agent2Agent (A2A) protocol sessions to manipulate victim agents.

Palo Alto Networks Unit 42 discovered agent session smuggling, a new attack technique in which a malicious AI agent exploits an established cross-agent session under the Agent2Agent (A2A) protocol to send covert instructions hidden among benign client requests and server responses. The technique leverages the implicit trust agents place in collaborating agents and the stateful, multi-turn nature of A2A sessions; the researchers stress it affects any stateful protocol, not an A2A flaw. Unlike one-shot data-based attacks, a rogue agent can converse, adapt and build false trust over multiple interactions. Proposed mitigations include human-in-the-loop enforcement, cryptographically signed AgentCards for remote agent verification, and context-grounding to detect injected instructions.

Palo Alto Unit 42 · 29d agoAI safety & security2