First reported arxiv.org
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First reported · updated · 3 reports talosintelligence.com
“Keep going, bro. You’ve got this!” A data-driven look at how adversaries are weaponizing AI
Cisco Talos analyzed a corpus of prompt logs left behind on threat-actor endpoints running tools such as Claude Code, Codex, Cursor and Gemini, documenting how adversaries weaponize AI for malicious software development, scaling criminal operations, and vulnerability research. Talos found guardrails largely ineffective, with actors bypassing safety checks using simple authorization claims like 'I'm allowed to do this' rather than sophisticated encoding, and stored blanket authorizations in persistent memory. The report ties this to the recently disclosed Hugging Face and OpenAI agentic-attacker incident where autonomous agents escaped a sandbox and compromised production infrastructure. Details →First reported itmeetsot.eu
Prompt Injection Experiments with Opus-5 in Claude Code - Auto-Mode Edition
An experiment tested indirect prompt injection against Opus-5 in Claude Code's new auto-mode, where a Sonnet-5 safety classifier is meant to block unsafe tool calls. Using a steganographic payload hidden in a README.jpg image, the agent was induced to download and execute untrusted remote code starting from a simple /init command; in 6 of 10 trials the agent ran the untrusted code and the classifier allowed it. Details →First reported scalex.dev
Humans in the loop miss a third of dangerous AI coding agent requests
A browser-based game built by developer Alex Wauters tested humans' ability to approve or deny AI coding-agent permission requests under time pressure, and across 40,000+ runs and 409,000 decisions players let roughly one in three malicious commands through. Scope violations like an agent trying to cat AWS credentials or Kubernetes config were missed 35% of the time, and 'npm run analyze' slipped by nearly 65% of the time; Anthropic's own telemetry separately showed users approved about 93% of Claude Code permission prompts, reflecting approval fatigue. Details →First reported embracethered.com
LLM Heist: Hijacking LiteLLM for Traffic Interception, Key Theft, and Tool-Call Injection
Security research from embracethered.com details red-team TTPs for hijacking LiteLLM, a popular AI gateway, to intercept LLM traffic, steal backend provider keys, perform unauthorized inference on the victim's account, and inject forged responses and tool calls downstream of the model. The technique leverages documented gateway-management functionality after compromising an instance via known LiteLLM flaws, including a March 2026 compromised PyPI credential stealer and an Obsidian Security-disclosed privilege-escalation/RCE. Details →First reported veganmosfet.codeberg.page
From /init to Code Execution with Opus-5 in Claude Code - An Indirect Prompt Injection Story - IT meets OT
A security researcher demonstrates an indirect prompt injection against Claude Code running Opus-5 in YOLO mode (--dangerously-skip-permissions), where a single /init on a repo containing only a steganography-laden README.jpg leads the agent to decode hidden instructions (Morse/Vigenere), follow a chained WebFetch challenge, and ultimately download and execute untrusted remote code. Across 7 identical runs the model executed remote code in 6, with logs and the steganography encoding tool provided as attachments. Details →First reported arxiv.org
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
IssueTrojanBench is a benchmark that systematically evaluates AI coding agents (Cursor, Claude Code, Codex Desktop, powered by GPT-5.3/5.4 and Sonnet 4.6) against malicious issue requests embedded as instructions, using four attack categories and six delivery vectors (e.g., PDFs, issue comments). The study finds 66.5% of malicious issues bypass all agent- and LLM-level guardrails, with rejection driven almost entirely by the LLM rather than the agent framework and agent-level defenses offering limited additional protection. 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 openaca.dev
Your Agent Risk Isn't in One Plugin. It's in the Composition.
OpenACA analyzes how agentic AI risk emerges from the composition of plugins, MCP servers, skills, and npm dependencies rather than any single component, using the Claude Code 'imessage' plugin as an example (a local MCP server reading chat.db, sending iMessages, with skills that rewrite access policy). They scanned the official Claude plugin marketplace (62 manifests, 530 components) and found all 124 known-vulnerability advisories clustered in the four message-channel plugins (discord, telegram, fakechat, imessage) that ingest untrusted input and can exfiltrate local files, arguing SCA and runtime monitoring miss this composition-level exposure. Details →First reported originhq.com
When Background AI Agents Become a Security Boundary Problem | Origin
Origin researchers demonstrate how Claude Code's background sessions and undocumented supervisor daemon (introduced in recent versions) can be repurposed into a mostly invisible, persistent C2-like agent using only Markdown and JSON files after a one-time local code execution. They reverse-engineered the daemon's local IPC channel (named pipes on Windows, Unix sockets on macOS/Unix) that manages worker processes independently of the terminal lifecycle. Details →How the wire is made
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