First reported catonetworks.com
Lead dispatch
First reported island.io
AgentBaiting: How Fake AI Skills Deliver Malware at Scale
Island security researchers uncovered the FakeGit campaign, which used roughly 7,600 malicious GitHub repositories—over 800 posing as AI Skills or MCP servers—to deliver SmartLoader malware that installs the StealC information stealer. The campaign introduces a technique called AgentBaiting, in which AI agents such as Claude Code, Gemini, and ChatGPT autonomously discover the attacker repositories, treat the malicious README as legitimate documentation, and pass installation instructions to users; the operation recorded over 14 million downloads.supply-chain · tool-abuse · malware-delivery · agentbaiting
mcp · ai-agents · ai-skills · llm · copilot
Severity
0.75
The wire · latest
First reported · updated · 3 reports appsentinels.ai
One Poisoned MCP Server Can Hijack All the Others — Coograph
MCP tool poisoning hides malicious instructions inside a Model Context Protocol server's tool descriptions, parameter schemas, or return values — invisible to human reviewers but fully read by the LLM, which then selects and executes the poisoned tool, enabling data exfiltration, credential theft, or lateral movement. The technique spans schema poisoning, tool shadowing, and rug pulls, and is catalogued as OWASP MCP03:2025; a single malicious server can influence an agent's decisions across all connected tools (cross-tool poisoning). Details →First reported substack.com
Data Exfiltration from Slack AI via indirect prompt injection
PromptArmor identified an indirect prompt injection vulnerability in Slack AI, a RAG-style chat search interface, where an attacker seeds poisoned tokens into a public channel or an imported document. When a user later queries Slack AI, the injected instructions cause private data (e.g. an API key from a private channel) to be embedded into a malicious Markdown link that exfiltrates the secret to the attacker's server when clicked. Details →First reported swarmnetics.com
Agentjacking and MCP trust: are AI coding agents too easy to steer?
Agentjacking, described by Swarmnetics and Tenet Security, abuses trusted error-report inputs in AI coding agents: an attacker with a publicly exposed Sentry DSN can inject malicious instructions into telemetry that the agent processes, steering it to exfiltrate secrets such as cloud keys, Git credentials, and private repo URLs. The root flaw is that these systems treat source trust as if it equals action trust, letting external report text become executable guidance. Details →First reported foxnews.com
HalluSquatting attack exploits AI hallucinations to spread malware
The HalluSquatting attack exploits AI hallucinations by registering package or software names that large language models invent when suggesting dependencies, so developers who trust AI-recommended names end up installing attacker-controlled malware. The technique weaponizes the tendency of LLMs to hallucinate plausible-sounding but nonexistent package names. Details →First reported · updated · 2 reports barracuda.com
Text salting: How hidden text evades AI email security
Barracuda researchers report more than one million retail-themed phishing emails since April that use 'text salting' — hidden, harmless-looking text invisible to recipients but readable by scanners — to dilute malicious signals and fool both traditional and AI-powered email security filters. Generative AI lets attackers generate these salted campaigns cheaply, at scale, and with high variation, while AI-based content analysis engines fail to see through the hidden content. Details →First reported paloaltonetworks.com
OpenClaw’s Skill Marketplace and the Emerging AI Supply Chain Threat
Unit 42 describes an emerging AI supply-chain threat in which malicious "skills" published to the OpenClaw agent skill marketplace act as a distribution channel for malware and data theft. Corroborating research from Trend Micro (Atomic macOS Stealer delivery), Bitdefender, Koi.ai (341 malicious ClawedBot skills) and JFrog documents how attackers hide payloads inside agent skills users install to extend AI-agent capabilities. Details →First reported wraith.sh
Data Exfiltration via Markdown Images: The Quiet AI Vulnerability
Wraith's attack guide by Anthony D'Onofrio details data exfiltration via markdown image rendering in AI products, where an injected payload (via prompt injection, RAG doc, shared document, or email) causes an LLM to emit a markdown image whose URL embeds secrets like system prompts, conversation history, or API keys; when the chat client renders the markdown, the browser silently fetches the attacker's URL, leaking the data with zero clicks and no visible artifact. The guide notes variants have hit ChatGPT, Microsoft Copilot, GitHub Copilot Chat, Slack AI Assistant, Google Bard, and Claude.ai, and covers four defensive patterns. Details →First reported sans.edu
Someone Is Scanning for Your MCP Servers and AI Assistant Credentials
A SANS ISC diary by Manuel Humberto Santander Peláez reports that analysis of 14 days of Apache/ModSecurity logs from a small web host revealed distributed internet scanning specifically targeting Model Context Protocol (MCP) servers, AI assistant configuration files, and locally exposed LLM endpoints. Notably, the POST /mcp probes carried valid JSON-RPC 2.0 MCP 'initialize' handshakes from 49 distinct source IPs, indicating scanners that speak the protocol and would enumerate tools and data sources if a real MCP server responded. Details →First reported · updated · 2 reports hiddenlayer.com
EchoGram and guardrail bypass: are AI defenses keeping up?
HiddenLayer research dubbed EchoGram demonstrates that carefully chosen token sequences can flip verdicts in LLM guardrail models, causing harmful prompts to be marked safe or benign prompts to trigger false alarms. The NHIMG editorial summarizes the finding and its implications for organizations relying on probabilistic AI safety layers to protect deployed LLMs and agents. Details →First reported elborai.me
Polymarket annotation injection
The author found injected annotations on a Polymarket event page that are rendered server-side and therefore visible to LLMs via web_search even when hidden in the browser. A planted annotation (source 'grok') contained a fake emergency-rate-cut message directing users to withdraw funds at a phishing-style domain, representing an indirect prompt-injection vector through Polymarket's annotation API endpoints. Claude's web search saw the content but correctly flagged it as phishing. Details →How the wire is made
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