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CVE-2026-26030: Semantic Kernel RCE via Prompt Injection

CVE-2026-26030: Semantic Kernel RCE via Prompt Injection

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 Microsoft Security Blog

Microsoft's Defender Security Research Team disclosed two CVEs in Semantic Kernel — a widely-used AI agent orchestration framework — demonstrating how prompt injection can escalate to remote code execution via compromised plugins. The vulnerabilities (CVE-2026-26030 and CVE-2026-25592) expose a systemic risk in the agentic AI layer: because frameworks like Semantic Kernel abstract tool orchestration, a single flaw in how LLM outputs are mapped to system tools can propagate across every application built on that foundation. This research signals a critical shift in AI threat modelling, where prompt injection is no longer a content risk but an execution risk.

CVE-2026-7482: Ollama Heap Read Exposes API Keys

CVE-2026-7482: Ollama Heap Read Exposes API Keys

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 SecurityWeek

A critical heap out-of-bounds read vulnerability (CVE-2026-7482, CVSS 9.3) in Ollama's GGUF model loader allows unauthenticated remote attackers to exfiltrate sensitive heap memory — including API keys, prompts, and PII — using just three API calls. With approximately 300,000 Ollama instances publicly exposed and no authentication required by default, the attack surface is immediately and broadly exploitable. The vulnerability has been patched in Ollama version 0.17.1, but unpatched internet-facing deployments remain at critical risk.

CrowdStrike Red Teaming: LLM Jailbreak and Data Poisoning

CrowdStrike Red Teaming: LLM Jailbreak and Data Poisoning

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 SecurityWeek

Joey Melo, Principal Security Researcher at CrowdStrike, outlines his methodology for AI red teaming, focusing on manipulating LLM guardrails through jailbreaking and data poisoning without altering underlying source code. His work, rooted in competitive AI hacking challenges, translates classical adversarial thinking into the emerging field of machine learning security. The profile highlights the growing professionalisation of AI red teaming as organisations seek to harden LLM deployments against real-world manipulation attacks.

Loopsy AI Agent Relay Enables Cross-Machine RCE

Loopsy AI Agent Relay Enables Cross-Machine RCE

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 HN AI Security

Loopsy is an open-source tool enabling cross-machine communication between AI coding agents (Claude Code, Cursor, Codex) and mobile devices via a self-hosted Cloudflare Workers relay. While designed for legitimate developer productivity, the architecture introduces significant attack surface: a relay brokering shell access and AI agent commands across machines is a high-value target for interception, hijacking, or supply chain compromise. Security teams should assess exposure before deploying such tools in sensitive development environments.

agent-desktop Prompt Injection Grants AI Agents OS Control

agent-desktop Prompt Injection Grants AI Agents OS Control

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

agent-desktop is an open-source Rust CLI tool that exposes full OS accessibility trees to AI agents, enabling programmatic control of any desktop application without screenshots or browser sandboxing. This dramatically expands the attack surface for agentic AI systems, as a compromised or prompt-injected agent could silently manipulate native applications, exfiltrate data, or perform destructive actions across the host OS. The tool's deterministic element references and structured JSON output make it trivially scriptable, lowering the barrier for AI-driven desktop abuse.

GPT-5.5 and Mythos Execute 32-Step Network Intrusion

GPT-5.5 and Mythos Execute 32-Step Network Intrusion

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.5 Ars Technica Security

The UK's AI Security Institute (AISI) found that OpenAI's GPT-5.5 matches Anthropic's Mythos Preview on cybersecurity benchmarks, including a 32-step simulated corporate network intrusion. Both models successfully completed the 'The Last Ones' data-extraction simulation — a first for any AI system — suggesting autonomous offensive cyber capability is a general frontier-model property, not a one-vendor breakthrough. The findings raise urgent questions about responsible release practices and the pace at which LLMs can independently execute multi-stage attacks.

Gemini CLI CVSS 10 RCE via Config Injection in CI/CD

Gemini CLI CVSS 10 RCE via Config Injection in CI/CD

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 The Hacker News

Google has patched a maximum-severity (CVSS 10.0) vulnerability in its Gemini CLI tooling that allowed unauthenticated attackers to achieve remote code execution by planting malicious configuration files in workspace directories automatically trusted by the agent in headless/CI mode. The flaw effectively weaponised CI/CD pipelines as supply chain attack paths, bypassing sandbox protections entirely before they could initialise. A secondary issue in '--yolo' mode further enabled prompt injection to trigger unrestricted shell command execution.

GPT-5.5 Matches Claude Mythos in Vulnerability Discovery

GPT-5.5 Matches Claude Mythos in Vulnerability Discovery

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Simon Willison

The UK's AI Security Institute has evaluated OpenAI's GPT-5.5 for offensive cybersecurity capabilities, finding it comparable to Anthropic's Claude Mythos model in identifying security vulnerabilities. Unlike Mythos, GPT-5.5 is generally available, meaning its vulnerability-discovery capabilities are accessible to a broad population including malicious actors. This raises significant concerns about the proliferation of AI-assisted exploitation tools at scale.

Llama Guard 4 Jailbreak Detection Vulnerable to Prompt Injection

Llama Guard 4 Jailbreak Detection Vulnerable to Prompt Injection

ATLAS OWASP LOW Limited impact · Standard review ▲ 7.2 Hugging Face Blog

Meta has released Llama Guard 4, a 12B multimodal safety classifier designed to detect and filter unsafe content in both image and text inputs/outputs for production LLM deployments. The model addresses jailbreak attempts and harmful content generation across 14 hazard categories defined by the MLCommons taxonomy. Alongside it, two lightweight Llama Prompt Guard 2 classifiers (86M and 22M parameters) target prompt injection and prompt attack detection.

Frontier LLMs Enable Industrialised Cyberattacks at Scale

Frontier LLMs Enable Industrialised Cyberattacks at Scale

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.5 Dark Reading

The article examines the emerging threat landscape posed by agentic AI systems in offensive security contexts, suggesting that frontier LLMs could enable industrialised exploitation at scale. Commentator Ari Herbert-Voss reframes the narrative, arguing this moment also presents a strategic opportunity for defenders. The piece surfaces tensions around autonomous AI-driven cyberattacks and their potential to outpace traditional security postures.

Supply Chain Risk: Gradio MCP Server Exposes AI Agents

Supply Chain Risk: Gradio MCP Server Exposes AI Agents

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.2 Hugging Face Blog

Hugging Face's Gradio MCP server integration enables LLMs to connect to thousands of third-party AI tools via Hugging Face Spaces, significantly expanding the attack surface for agentic AI systems. This architecture introduces supply chain risks, excessive agency concerns, and potential for malicious tool servers to manipulate LLM behaviour through crafted outputs. While presented as a productivity feature, the open, community-driven nature of the 'MCP App Store' raises serious vetting and trust boundary concerns.

Anthropic Mythos Preview Breached via Contractor Credentials

Anthropic Mythos Preview Breached via Contractor Credentials

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Wired Security

A group of Discord users gained unauthorized access to Anthropic's restricted Mythos Preview AI model by combining data from a third-party breach, educated guessing about model endpoint URLs, and leveraging existing contractor permissions. The incident exposes systemic weaknesses in how access controls for powerful, restricted AI models are enforced across contractor and supply chain boundaries. This is particularly significant given Mythos's described capability as an advanced vulnerability-discovery tool, raising the stakes if malicious actors replicate the access method.

Model Extraction Attacks Surge: Google GTIG Q4 Report

Model Extraction Attacks Surge: Google GTIG Q4 Report

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 Mandiant Blog

Google Threat Intelligence Group's Q4 2025 AI Threat Tracker documents a meaningful escalation in adversarial AI misuse, including a surge in model extraction (distillation) attacks, nation-state operationalisation of LLMs for phishing and reconnaissance, and the emergence of AI-integrated malware families such as HONESTCUE that leverage Gemini's API. While no breakthrough capabilities have been observed from APT actors, the integration of agentic AI for tooling development signals a maturing threat landscape. Defenders should prioritise monitoring for model extraction activity, API abuse, and AI-augmented social engineering campaigns.

Stash AI Memory Poisoning Exposes Agent Data Leakage

Stash AI Memory Poisoning Exposes Agent Data Leakage

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 HN AI Security

Stash is an open-source persistent memory layer for AI agents using PostgreSQL and pgvector, exposing a broad MCP tool surface (28 tools) that introduces significant attack vectors including memory poisoning, sensitive data leakage, and cross-namespace contamination. While marketed as a productivity enhancement, the architecture centralises long-term agent memory in a shared backend, creating a high-value target for adversarial manipulation. Security teams deploying autonomous agents should treat persistent memory stores as critical infrastructure requiring strict access controls and integrity validation.

OpenAI Codex CLI Credentials Hijacked via Malicious Package

OpenAI Codex CLI Credentials Hijacked via Malicious Package

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

A new Python package, llm-openai-via-codex 0.1a0, explicitly 'hijacks' Codex CLI credentials to route API calls through an unofficial OpenAI endpoint, bypassing standard API billing and access controls. This represents a credential misuse pattern that could expose organisations to unauthorised API access and quota theft. The technique exploits an undocumented or semi-official API surface, raising supply chain and access control concerns for enterprise OpenAI deployments.

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