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First reported 22 Sep · updated yesterday · 5 reports talosintelligence.com

The Closed Quorum: Inside the first reported autonomous AI C2 implant

Cisco Talos documented CLOSEDQUORUM, a Windows implant it describes as the first publicly reported autonomous AI command-and-control (C2) malware, discovered via its CAIRN project. The binary delegates its next-action decisions to a quorum of up to four commercial LLMs (DeepSeek, Qwen, Mistral, and Google Gemini) queried in sequence, executing their chosen actions to harvest credentials and crypto wallets without a human operator or dedicated C2 server; Talos has no confirmation of in-the-wild deployment but linked the developer to carding forum postings.

autonomous-agent · ai-c2 · malware · data-exfiltration · tool-abuse
llm · ai-agents · windows

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First reported 20 Aug arxiv.org

The Injection Paradox: Brand-Level Suppression in Safety-Trained LLM Recommendations via RAG Context Injection

An ICML 2026 workshop paper by Hyunseok Paeng, "The Injection Paradox," reports a reproducible failure mode in RAG-based LLM recommendation where prompt injections embedded in retrieved documents backfire, suppressing the injected brand below baseline in safety-trained Claude models (Claude Opus 4.6 dropped the target brand from a 54% baseline to zero top-2 recommendations). The suppression propagates to unmodified documents of the same brand, while GPT models showed the opposite effect, raising a reverse-attack scenario where an adversary poisons a competitor's documents to suppress that competitor's brand.

First reported 12 Aug sciencedirect.com

A systematic literature review of large language models in phishing attack generation and detection

A systematic literature review (PRISMA methodology, 36 studies from 2023-2025) examines the dual role of large language models in both generating and detecting phishing attacks. The review finds LLMs, especially GPT-based models, lower the technical barrier for attackers by producing coherent, persuasive phishing email and website content, while also strengthening detection through analysis of textual and visual content, often outperforming traditional machine-learning approaches.

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