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Microsoft Copilot MCP Tool Poisoning Enables Data Exfiltration

Microsoft Copilot MCP Tool Poisoning Enables Data Exfiltration

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 9.1 The Hacker News

Microsoft researchers have demonstrated how attackers can embed hidden instructions inside MCP tool descriptions to covertly redirect AI agents into exfiltrating sensitive business data. Because each individual action the agent takes appears legitimate — using approved tools and the user's own permissions — default security controls generate no alerts. The attack exploits a fundamental design tension in MCP: tool descriptions simultaneously carry operational instructions and attacker-controlled data, collapsing a critical trust boundary.

Google DeepMind Releases AI Agent Attack Taxonomy

Google DeepMind Releases AI Agent Attack Taxonomy

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.7 SecurityWeek

Google DeepMind researchers have released a structured taxonomy categorising adversarial attacks against autonomous AI agents into six classes — content injection, semantic manipulation, cognitive state poisoning, behavioural control, systemic, and human-in-the-loop traps — formalising an emerging threat model for agentic AI systems. For defenders, this framework codifies attack paths that exploit the agent's inability to distinguish trusted instructions from attacker-controlled data ingested from web pages, emails, documents, and tool outputs. NIST evaluation data cited in the research shows malicious instruction injection succeeded in 57% of tested agent hijacking scenarios on average, underscoring that these are active, high-yield attack vectors rather than theoretical concerns.

Bayer and Thoughtworks Ship PRINCE Agentic RAG Platform

Bayer and Thoughtworks Ship PRINCE Agentic RAG Platform

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 HN AI Security

Bayer AG and Thoughtworks have published a detailed case study on PRINCE, a production agentic RAG system combining multi-agent orchestration, Text-to-SQL, and human-in-the-loop workflows to answer complex pharmaceutical preclinical research questions and draft regulatory documents. The system's architecture — spanning intent clarification, planning, retrieval, reflection, and writing agents with access to decades of safety study data — introduces a broad attack surface including prompt injection across agent boundaries, SQL injection via natural language, and sensitive data exfiltration through compromised agent outputs. Defenders evaluating similar agentic platforms should treat each inter-agent handoff as a trust boundary requiring independent validation and focus on data leakage controls given the sensitivity of preclinical regulatory data.

GitHub Ships Data Analytics Agent Built on Copilot

GitHub Ships Data Analytics Agent Built on Copilot

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.8 GitHub Blog

GitHub has published a detailed engineering account of how it built an internal data analytics agent using GitHub Copilot, exposing the architectural patterns — including natural language-to-SQL translation, autonomous tool invocation, and internal data access — that underpin such systems. For defenders, this blueprint highlights concrete risks around prompt injection into analytics pipelines, excessive agency over sensitive internal datasets, and the challenge of auditing LLM-generated queries before execution. Organisations adopting similar agentic analytics patterns should treat this as a reference threat model rather than a safe-to-copy architecture.

Google Launches Android 17 with Gemini Omni Integration

Google Launches Android 17 with Gemini Omni Integration

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 TechCrunch AI

Android 17 embeds Gemini Omni and multiple AI models (Lyria 3, AudioLM) directly into OS-level functions including video editing, call handling, screen recording, and emergency detection, dramatically expanding the attack surface for AI-assisted exploitation on mobile endpoints. The deep integration of conversational AI with device sensors, media pipelines, and inter-app communication creates novel prompt injection and data exfiltration vectors that existing mobile threat defences were not designed to address. The simultaneous AirDrop interoperability expansion and cross-device Pixel Watch mirroring further widen the lateral movement surface across the Google hardware ecosystem.

OpenClaw Agent Vulnerable to Prompt Injection RCE

OpenClaw Agent Vulnerable to Prompt Injection RCE

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 The Hacker News

Two independent research teams demonstrated that OpenClaw, a self-hosted AI agent, is vulnerable to prompt injection attacks delivered through shared contacts, vCards, location pins, and plain emails — enabling attacker-controlled code execution and sensitive data exfiltration. Imperva's finding, now patched in version 2026.4.23, exploited the agent's failure to mark message objects as untrusted before passing them to the underlying LLM. Varonis separately showed that a single crafted email could instruct an agent to forward mock AWS credentials and customer data to an external address, a behaviour-level risk no patch can fully remediate.

OpenClaw AI Agent Vulnerable to Phishing, Leaks AWS Credentials

OpenClaw AI Agent Vulnerable to Phishing, Leaks AWS Credentials

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 BleepingComputer

Varonis Threat Labs demonstrated that the OpenClaw open-source AI agent framework is vulnerable to social engineering attacks analogous to those used against human targets, successfully tricking the agent into exfiltrating AWS credentials, database secrets, and CRM exports to attacker-controlled addresses. The research tested two LLMs (Gemini 3.1 Pro and GPT-5.4) across generic and phishing-aware configurations, finding that even the hardened profile did not fully prevent data leakage. These findings highlight that autonomous AI agents with broad tool access and insufficient identity verification represent a significant and largely unaddressed attack surface in enterprise environments.

ChatGPT Prompt Injection Enables Data Exfiltration

ChatGPT Prompt Injection Enables Data Exfiltration

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 Simon Willison

OpenAI has rolled out 'Lockdown Mode' for ChatGPT personal and self-serve business accounts, a deterministic control designed to block the data exfiltration leg of prompt injection attacks. The feature directly addresses the 'Lethal Trifecta' — the combination of private data access, untrusted content exposure, and an outbound exfiltration channel — by restricting outbound network requests at the infrastructure level rather than relying on AI-evaluated guardrails. Critically, OpenAI's own documentation acknowledges the feature's existence implies that default ChatGPT settings do not robustly prevent determined data exfiltration attacks.

mouse5212-super-formatter npm Malware Steals Claude Files

mouse5212-super-formatter npm Malware Steals Claude Files

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 The Hacker News

A malicious npm package named 'mouse5212-super-formatter' was discovered exfiltrating files from Anthropic's Claude AI user directory by authenticating to a threat actor-controlled GitHub repository. The package disguised itself as a legitimate archive utility while silently uploading all local workspace files during the postinstall phase. Notably, the attacker's poor operational security — including a leaked GitHub token — suggests AI-generated malware with minimal human oversight, pointing to a growing trend of low-skill threat actors leveraging AI to produce supply chain malware.

Gemini Spark Prompt Injection Exposes Enterprise Gmail Data

Gemini Spark Prompt Injection Exposes Enterprise Gmail Data

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 Simon Willison

Google's newly announced Gemini Spark personal AI agent, integrated with Gmail, Drive, Calendar, and other sensitive Google services, presents a significant prompt injection attack surface as it processes user data at scale. The article highlights that Google's published security mitigations — ephemeral VMs, Agent Gateway, and DLP policies — address infrastructure isolation but do not directly address the prompt injection vector inherent to LLM-powered agents processing untrusted content. Additionally, the transition from open-source Gemini CLI to a closed-source Antigravity CLI raises supply chain transparency concerns.

Microsoft RAMPART Tests AI Agents for Prompt Injection

Microsoft RAMPART Tests AI Agents for Prompt Injection

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 The Hacker News

Microsoft has released two open-source tools, RAMPART and Clarity, aimed at embedding security testing into AI agent development workflows. RAMPART extends the existing PyRIT framework with a Pytest-native harness for running adversarial and safety tests against AI agents, explicitly covering cross-prompt injection, data exfiltration, and behavioural regression scenarios. Clarity operates as a pre-code design analysis tool, helping teams surface and challenge unsafe assumptions before an agentic system is built.

Steganography in LLMs Enables Covert Data Exfiltration

Steganography in LLMs Enables Covert Data Exfiltration

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 Schneier on Security

Research highlighted by Bruce Schneier confirms that LLMs are highly effective at embedding hidden messages within seemingly normal text, a technique known as text-in-text steganography. This capability raises significant concerns for covert communications, data exfiltration, and the evasion of AI content moderation systems. Even small models with ~4 billion parameters demonstrate robust encoding and decoding of obfuscated language, lowering the barrier for adversarial misuse.

Zealot: Autonomous LLM Cloud Penetration Testing System

Zealot: Autonomous LLM Cloud Penetration Testing System

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.0 Palo Alto Unit 42

Unit 42 researchers built 'Zealot,' a multi-agent LLM-powered penetration testing system capable of autonomously executing end-to-end offensive operations against cloud infrastructure, demonstrating that AI acts as a significant force multiplier for cloud attacks. The system successfully attacked a misconfigured GCP sandbox environment using a supervisor-coordinated architecture of specialist agents, validating that agentic AI can operate at machine speed against real cloud misconfigurations. This research follows Anthropic's November 2025 disclosure of a state-sponsored AI-orchestrated espionage campaign and marks a critical inflection point in understanding autonomous AI offensive capabilities.

ChatGPT Code Runtime Exfiltrates Data via Prompt Injection

ChatGPT Code Runtime Exfiltrates Data via Prompt Injection

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 Check Point Research

Check Point Research disclosed a critical vulnerability in ChatGPT's code execution runtime that allows a single malicious prompt to establish a covert outbound exfiltration channel, bypassing OpenAI's stated network isolation safeguards. Sensitive user data — including uploaded files, conversation content, and personal documents — could be silently transmitted to attacker-controlled servers without user knowledge or consent. The same channel was also found capable of enabling remote shell access within the Linux execution environment.

Vertex AI Privilege Escalation Exposes GCP Credentials

Vertex AI Privilege Escalation Exposes GCP Credentials

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 Palo Alto Unit 42

Unit 42 researchers discovered critical privilege escalation and data exfiltration vulnerabilities in Google Cloud Platform's Vertex AI Agent Engine, demonstrating how a deployed AI agent can be weaponized to compromise an entire GCP environment through excessive default permissions on service agents. By exploiting the P4SA (Per-Project, Per-Product Service Agent) default permission scoping, attackers could extract service agent credentials and gain privileged access to consumer project data and restricted producer project resources within Google's own infrastructure. Google has since updated its documentation in response to the coordinated disclosure.

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