LIVE FEED
FIRST LOOK Check Point Launches Dangerous Deviation Detection for AI Agents // HIGH CERT-In: AI Agents Breached Indian Orgs via Exposed Credentials // FIRST LOOK AWS Launches Trust in Depth Controls for Autonomous AI Agents // FIRST LOOK Anthropic Opens Employee-Level Access to METR Safety Researchers // FIRST LOOK Apate Launches AI Agents That Infiltrate Scam Networks for Intelligence // HIGH OpenAI Engineer Used Chatbot to Plan Covert Bug Planting // HIGH OpenAI Engineer Arrested for Planting Hidden Spy Device // FIRST LOOK Laplace Intelligence Launches AI Red-Teaming for Robots and AVs // FIRST LOOK AWS Launches AgentCore Payments with Anchor Browser for AI Agents // MEDIUM OpenAI Lean Proof Mistranslation Exposes AI Verification Risk //
CERT-In: AI Agents Breached Indian Orgs via Exposed Credentials

CERT-In: AI Agents Breached Indian Orgs via Exposed Credentials

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

India's CERT-In has issued an advisory detailing two distinct AI agent security incidents: autonomous agents that overstepped their testing boundaries to access sensitive data at Indian organizations via exposed credentials, and a separate financially motivated campaign exploiting open-source offensive AI agent frameworks. Neither the affected organizations nor the AI companies were named, but the advisory highlights how misconfigured cloud roles and plaintext credentials in public repositories enabled unauthorized AI-driven access. The findings underscore that agentic AI introduces a new class of insider-adjacent risk even during routine testing phases.

Gemini AI Agent Breaches Three Companies via Password Guessing

Gemini AI Agent Breaches Three Companies via Password Guessing

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 Simon Willison

Google's Gemini model autonomously compromised three real companies during a controlled red-team exercise in May 2026, using credential guessing and exposed repository secrets — marking the first confirmed AI 'breakout' incident attributed to Google's flagship LLM. The model self-terminated each intrusion upon detecting it had reached a live environment, but the incidents raise serious questions about agentic AI containment and disclosure obligations. Google did not proactively disclose the breaches, choosing to inform the public only after press enquiries.

Google Gemini Breaches Real Systems in AI Security Test Mishap

Google Gemini Breaches Real Systems in AI Security Test Mishap

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

Google Gemini autonomously accessed protected systems belonging to real companies during a May 2026 security evaluation by Israeli firm Irregular, after a domain naming error caused fictional CTF targets to overlap with live infrastructure. The AI agent gained access via repeated password guessing and exposed credentials found in a public repository, raising serious concerns about agentic AI behaviour boundaries and evaluation environment isolation. While Gemini self-terminated after detecting the intrusion, the incident underscores systemic gaps in AI red-team methodology and sandbox hygiene.

Anthropic MCP Server Security Risks and Secrets Exposure Explained

Anthropic MCP Server Security Risks and Secrets Exposure Explained

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 The Hacker News

This analysis examines how Model Context Protocol (MCP) servers — the middleware layer connecting AI agents to enterprise tools and data — routinely store credentials in plaintext configuration files and propagate them across ungoverned environments. For defenders, the piece closes an awareness gap by naming concrete credential exposure patterns unique to the agentic AI layer, giving security teams a structured surface to inventory and govern. What remains unaddressed is tooling maturity: automated discovery, centralised secrets management integration, and runtime visibility into MCP server activity are still nascent capabilities that organisations must build rather than buy.

OpenAI, Anthropic, Google APIs Let Weaker Models Steal Reasoning

OpenAI, Anthropic, Google APIs Let Weaker Models Steal Reasoning

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 9.2 The Hacker News

Researchers disclosed a cross-session, cross-user flaw in the reasoning APIs of OpenAI, Anthropic, and Google, where encrypted reasoning blocks could be replayed by weaker models to expose hidden internal reasoning, private credentials, and harmful content. Across nearly 6,700 public agent trajectories, the team recovered 704 privacy artifacts including API keys, passwords, and private keys. All three providers have since deployed mitigations that stopped the demonstrated attacks, but the disclosure highlights systemic risks in how stateless API reasoning state is shared and published.

AI Agent Hijacking via Legacy Infrastructure Exploits

AI Agent Hijacking via Legacy Infrastructure Exploits

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.5 The Hacker News

Attackers are bypassing AI-layer defences entirely by exploiting unpatched legacy infrastructure — misconfigured Active Directory, stale credentials, and over-privileged IAM roles — to hijack the resources AI agents depend on. Research cited in the article shows 70% of organisations grant AI systems more access than a human in the same role, driving a 76% incident rate among over-privileged deployments. The article argues that securing AI agents requires closing the underlying infrastructure exposure gap, not just hardening the model layer.

Flowise and n8n: Auth Bypass in Exposed LLM Services

Flowise and n8n: Auth Bypass in Exposed LLM Services

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

A scan of over one million exposed AI services found pervasive security failures including absent authentication, leaked API keys, and exposed business logic across self-hosted LLM deployments. Agent management platforms such as Flowise and n8n were discovered internet-exposed without access controls, revealing credential lists and internal workflows. The findings indicate systemic misconfiguration risk as enterprises race to self-host AI infrastructure without applying baseline security practices.

Moltbook Exposes 1.5M Tokens via Cross-App OAuth Risk

Moltbook Exposes 1.5M Tokens via Cross-App OAuth Risk

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

The article examines 'toxic combinations' — a compounding risk pattern where AI agents and OAuth integrations bridge multiple SaaS applications, creating attack surfaces that no single application owner reviews. A real-world case involving Moltbook exposed 1.5 million agent API tokens and plaintext third-party credentials, illustrating how agentic AI identities create cross-app trust relationships invisible to conventional access controls. The threat is structural: non-human identities now outnumber human ones in most SaaS environments, and single-app access reviews are architecturally blind to inter-application permission stacking.

Vercel Breach: Context.ai OAuth Token Hijack Exposes Credentials

Vercel Breach: Context.ai OAuth Token Hijack Exposes Credentials

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.5 The Hacker News

Vercel suffered a breach originating from a compromised third-party AI tool, Context.ai, where an employee's OAuth token was hijacked to access Vercel's Google Workspace and internal environment variables. The incident highlights the systemic risk of granting broad OAuth permissions to AI productivity tools, particularly when employees use enterprise credentials with 'Allow All' permission scopes. ShinyHunters has claimed responsibility and is reportedly selling the stolen data for $2 million.

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