First reported youtube.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 arxiv.org
Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses
Researchers at Penn State introduce FARMA (Forged Amplifying Rationale Memory Attack), which poisons an LLM agent's remembered reasoning history rather than its factual knowledge, inserting forged reasoning traces with evasive language that bypass keyword filters and self-referential reinforcement that defeats consensus-based defenses like A-MemGuard, achieving up to 100% attack success. They also present SENTINEL, a layered defense pipeline whose Reasoning Guard structurally analyzes entries using five weighted signals, reducing FARMA's success rate to as low as 0% with no false positives across 326 benign traces. Details →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 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 medium.com
Prompt Injection Is No Longer Just a Chatbot Problem
A Medium explainer titled "Prompt Injection Is No Longer Just a Chatbot Problem" argues that as AI applications gain the ability to search documents, retain memory, call APIs, and run autonomous agent workflows, prompt injection now extends beyond chatbots to poison retrieved knowledge, corrupt long-term memory, manipulate an agent's plan, or trigger unauthorized tool calls. The piece frames prompt injection as a core security challenge across prompts, RAG, tools, memory, and agents. Details →First reported aclanthology.org
Data Exfiltration via Backdoored Tool Use
Researchers present Back-Reveal, a data exfiltration attack that embeds semantic triggers into fine-tuned tool-use LLM agents; when triggered, the backdoored agent invokes memory-access tool calls to retrieve stored user context and covertly transmits it to an attacker-controlled server disguised as legitimate retrieval/search API requests. The paper shows multi-turn interaction amplifies leakage, as attacker-controlled retrieval responses steer subsequent agent behavior to induce cumulative disclosure, and evaluates defenses like NeMo Guardrails and LLM Guard as targets. Details →First reported lakera.ai
Memory poisoning in AI agents: are your controls keeping up?
An NHIMG editorial based on Lakera research discusses memory poisoning and long-horizon goal hijacks in AI agents, where attackers persistently alter what agents remember and optimize for, creating silent compromise paths that unfold across sessions. The piece offers governance guidance such as treating persistent memory as governed state, tracing provenance on memory items, and monitoring multi-step workflows for objective drift. 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 detect.fyi
Detection Engineering in the Era of Semantic Malware | by Koifsec | Jul, 2026
A detection-engineering analysis examines "semantic malware" / "promptware" — malware delivered through prompt injection rather than binaries — using Origin's Brainworm PoC (a poisoned CLAUDE.md file that hijacks AI coding assistants into registering with a C2 server over RabbitMQ) and the Ben-Gurion/Tel Aviv/Harvard/Toronto "Promptware Kill Chain" arXiv paper as anchors. The kill chain formalizes seven stages (initial access via prompt injection, jailbreaking, reconnaissance, memory/retrieval poisoning, command-and-control, lateral movement, actions on objective) across 36 documented incidents, and the piece discusses how defenders can detect such trust-boundary failures. Details →First reported agentsafelabs.com
Prompt Injection Is Not a Chatbot Problem: How the Attack Surface Changes When Your LLM Has Tools
An explainer arguing that chatbot-era prompt injection defenses (input filtering, output monitoring, instruction hierarchy, adversarial training) fail against agentic systems because injection enters through tools, retrieved documents, memory, API responses, and inter-agent instructions rather than the user message. It frames the issue as a system-architecture and trust-boundary problem rather than a model-tuning one. Details →First reported freyzo.github.io
deep-xpia - multi-hop cross-prompt injection benchmark
deep-xpia is a benchmark of multi-hop cross-prompt injection (DXPIA) across delegated agent boundaries, with 300 live-measured cases and 8 attack patterns showing 69% land undefended and 12% even with all defenses. It highlights registry injection at tool-discovery (DXPIA-008) entering upstream of all 5 stacked defenses and maps patterns to documented Copilot incidents like EchoLeak. Details →How the wire is made
Poll & cluster
Internet is crawled for AI security news and near-duplicate coverage is embedded and grouped into durable items.
Curate
AI Agent filters for agentic-AI relevance, classifies and tags each item, scores severity for threats, and writes the summary.
Every item here is one machine-curated intelligence object, not a headline.
Read the wire for free. There is a small charge to ask the index questions.
The wire, open
The complete curated feed, no key required.
- GET /feed.xml — RSS 2.0, every item
- GET /api/items — read-only
The vector desk
Query the index by meaning, not just keyword.
- GET /api/items?tags=&minSeverity=&itemType=
- GET /api/search?q= — keyword
- GET /api/semantic?q= — vector