First reported arxiv.org
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 aws.com
Memory Poisoning Attack in AI Agents - Attack vectors and defenses on AWS | AWS Builder Center
An AWS Builder Center article titled "Memory Poisoning Attack in AI Agents" discusses attack vectors and defenses for memory poisoning against AI agents on AWS. The retrievable page body contains only cookie-consent boilerplate, so the substantive technical content could not be analyzed. Details →First reported arxiv.org
Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems
"Bad Memory" is a research paper studying prompt injection attacks in memory-based agentic systems using a sandboxed synthetic workspace, evaluating Anthropic Claude Code and OpenAI Codex across four models. The authors find that while it is hard to make an agent overwrite its own memory files using untrusted external content, payloads already planted in persistent memory files can successfully compromise current and future sessions, with attack success and persistence varying by system, model, and adversarial goal. Details →First reported icml.cc
ICML Poster An Empirical Study of Memory Poisoning Defenses for LLM Agents
An ICML 2026 poster titled "An Empirical Study of Memory Poisoning Defenses for LLM Agents" presents research evaluating defenses against memory poisoning attacks on LLM agents, with an associated code artifact (AMemGuard on GitHub). Details →First reported koreshield.ai
Reproducing an Indirect Prompt Injection Against a RAG Pipeline
A Koreshield write-up reproduces an indirect prompt injection against a RAG customer-support pipeline, where a poisoned knowledge-base document (doc-002) contains a bracketed fake 'system note' instructing the model to dump all retrieved documents and exfiltrate internal tokens with an 'EXFIL::' marker while hiding the action. The author provides runnable code, a public GitHub harness (UncleTeslim/prompt-injection-repro), and real model logs demonstrating the attack against a local Llama3-8B model. Details →How the wire is made
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