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AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection

Researchers found a vulnerability (CVE-2026-10591) in AWS Kiro, an agentic IDE, where hidden instructions planted in a web page or source file that Kiro processes can trigger indirect prompt injection to rewrite Kiro's own MCP server configuration (~/.kiro/settings/mcp.json) or allowlist arbitrary Bash commands in .vscode/settings.json, achieving arbitrary code execution on the developer's machine with no approval prompt. The human-in-the-loop approval boundary is bypassed because Kiro can write to these config files without user consent, and AWS has issued a fix and CVE.

indirect-prompt-injection · prompt-injection · remote-code-execution · tool-abuse · config-poisoning
ai-agents · mcp · llm · agentic-ide

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CVE-2026-35029 Exploited for Full Server Takeover

CVE-2026-35029, a critical broken access control flaw in the LiteLLM AI gateway (versions before 1.83.0, CVSS 8.8), is being actively exploited to read sensitive files and achieve full server takeover. Attackers abuse the unauthenticated /config/update route to point UI_LOGO_PATH at files like /app/.env or /proc/self/environ and retrieve their plaintext contents via /get_image, and can overwrite admin credentials and register malicious pass-through proxy routes to exfiltrate model API secrets. Honeypot telemetry recorded roughly 3,900 requests from 73 distinct IPs targeting admin routes; the fix is version 1.83.0. Details →

Maland | Another Cursor 0-day Enabling Arbitrary Code Execution Beyond Git.exe

Cursor 3.0.0 patches CVE-2026-48124, a class of sandbox-to-host code execution weaknesses affecting AI coding agents including Cursor, OpenAI Codex CLI, Google Gemini CLI, and Antigravity, where files written by a sandboxed agent are later consumed and executed by trusted host tooling (extensions, task runners, Git integrations, Docker). Related research by Mindgard and others details a Windows binary-planting flaw where opening a repository auto-executes a malicious git.exe planted at the repo root, yielding zero-click arbitrary code execution with no prompt injection or model in the loop. Details →

NVD - CVE-2026-62676

CVE-2026-62676 is a HIGH-severity (CVSS 7.1) flaw in Omnigent, an open-source AI agent framework and meta-harness for orchestrating coding agents. Prior to 0.3.0, the shared shell-command parser in _shell.py fails to recognize combined interpreter flags, timeout/nice/setsid/stdbuf wrappers, command substitutions, and a background control operator, so a gated git push or gh write hidden in these forms produces no parsed operation, causing the repo/branch allowlist and workspace confinement policies to abstain and allow the command. An authenticated or prompt-injected agent can push to an unauthorized repository or branch or escape the intended workspace; fixed in version 0.3.0. Details →

Defending Against an Active Threat to Siemens S7 Series PLCs

Five US federal agencies (NSA, CISA, FBI, DOE, EPA) issued advisory AA26-231A warning of an active threat in which attackers use AI coding assistants together with open source industrial libraries (snap7.dll/python-snap7) to generate custom exploitation scripts disguised as legitimate OT monitoring tools against internet-exposed Siemens S7 Series PLCs at water, energy, manufacturing, and other critical facilities. The AI-generated tools provide read/write access to PLC memory, configuration, and ladder logic via the S7comm protocol, and the activity is suspected to be linked to Iran-affiliated operatives. Details →

AWS, Google, and Vercel Agent Flaws Let Attackers Trigger Tools Without Running the Model

Security flaws in agent infrastructure from AWS (Bedrock AgentCore's InvokeHarness API), Google (Agent Development Kit for Python), and Vercel (AI SDK harness packages for Codex and OpenCode) allow untrusted or forged instructions to reach an agent's tools without verifying that a model turn authorized them. In several attack paths the model never ran, so system prompts, content filters, and model-level guardrails never engaged. AWS fixed the managed service, Google addressed the issues in ADK 2.5.0, and Vercel patched @ai-sdk/harness-codex 1.0.29 and @ai-sdk/harness-opencode 1.0.28. Details →

IBM's agentic AI platform is under active attack - patch now

A critical vulnerability, CVE-2026-9198, in IBM-owned low-code AI agent builder Langflow lets unauthenticated attackers achieve remote code execution on default deployments by chaining an auto-login endpoint that mints superuser tokens with a code-validation endpoint that runs arbitrary Python. CISA added it to its Known Exploited Vulnerabilities catalog after confirming active exploitation; IBM says Langflow OSS 1.0.0 through 1.10.0 are affected and urges upgrading to 1.10.1 or later. Sysdig telemetry documents in-the-wild attack chains (including related CVE-2026-33017 RCE and CVE-2026-55255 IDOR) that probe /api/v1/auto_login, enumerate flows, and deliver second-stage loaders. Details →

Paperclip AI Flaws Let Attackers Run Host Commands via Malicious Agent Imports

Two flaws in Paperclip, an open-source control plane for teams of AI agents, let attackers execute host commands by importing and starting a malicious agent. The server-side path CVE-2026-41679 (CVSS 10.0) needs no account or victim interaction against authenticated deployments with default registration, while GHSA-x8hx-rhr2-9rf7 (CVSS 9.6) triggers when a user opens an attacker-controlled page in default local_trusted mode; a third flaw exposed sensitive data via API routes lacking access checks. Version v2026.416.0 adds import-authorization and hostname-validation fixes. Details →

Hugging Face Diffusers Flaws Could Let Model Repositories Execute Arbitrary Code

Zafran Labs disclosed three high-severity flaws, collectively named FaceHugger, in Hugging Face's Diffusers library (CVE-2026-44827 CVSS 8.8, CVE-2026-45804 CVSS 7.5, and CVE-2026-44513 CVSS 8.8) that let a malicious model repository silently execute arbitrary code on any machine loading it. The flaws stem from a Time-of-Check to Time-of-Use race that bypasses the trust_remote_code safeguard by splitting a model download into two non-atomic HTTP requests, so a routine model load becomes an initial-access vector across CI/CD, container, and production pipelines. Details →
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