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First reported arxiv.org
(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents
Researchers from Simon Fraser University, CUHK, Shandong University, and QAX's Xingtu Lab published an arXiv paper (arXiv:2607.00333) demonstrating seven concrete attacks against five open-source mobile AI agent frameworks (AppAgent, AppAgentX, Mobile-Agent-v3, Open-AutoGLM, and MobA). A malicious Android app without privileged permissions can slip invisible on-screen text that the VLM-driven agent reads and acts on, exploiting a 'Screen Perception' surface (human-vs-machine vision gap) and a 'Misused Channel' surface to hijack agent actions and even achieve arbitrary command execution on the host PC driving the agent. All five frameworks fell to at least six of the seven attacks; no CVE was assigned and authors report no evidence of in-the-wild use. Details →First reported google.com
Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting
Researchers from Tel Aviv University, Technion, and Intuit disclosed 'HalluSquatting' (adversarial hallucination squatting), a technique that exploits the predictable tendency of LLMs to hallucinate resource identifiers (repos, skills, URLs) in tool calls. By preemptively registering the hallucinated resources, attackers can achieve scalable, untargeted remote tool execution and remote code execution across popular agentic LLM applications without any direct injection channel, potentially building agentic botnets. Details →First reported · updated · 2 reports arxiv.org
Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions
The paper 'Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions' systematically analyzes vulnerabilities in Model Context Protocol (MCP) servers, finding that taint-style vulnerabilities (e.g., SSRF as in the Markdownify server, CVE-2025-5276) make up a substantial fraction and are slow to be remediated. It proposes SpellSmith, which builds tool-level risk profiles and embeds behavioral guidance into MCP tool descriptions plus LLM self-reflection to mitigate exploitation without code-level fixes. Details →First reported dev.to
How I Used Automated Red Teaming to Evaluate My AI Agent's Safety - DEV Community
A DEV Community walkthrough demonstrates using automated red teaming (the Strands Evals red-teaming module with AdversarialCaseGenerator and CrescendoStrategy multi-turn escalation) against an internal helper AI agent built on Strands Agents and Amazon Bedrock. The author shows how a bash-equipped agent can be coaxed via gradual multi-turn escalation into reading AWS credentials and how auto-generated adversarial cases surface data-exfiltration, excessive-agency, and system-prompt-leak breaches, going from 6/9 detected breaches to 0 after adding guardrails. Details →First reported tracebit.com
Now, defenders are embracing the prompt injection, too
Researchers at Tracebit disclosed a defensive technique they call "context bombing," in which prompt injections placed alongside decoy AWS secrets trigger an attacking LLM's own guardrail refusal mechanism, causing autonomous AI hacking agents to shut down. Across 152 attack runs against five models (Opus 4.8, Gemini 3.1 Pro, GLM 5.2, DeepSeek 4 Pro, Kimi 2.6), planting a forbidden-content string cut full account admin compromise from 57% to 5% and complete compromise with persistence from 36% to 1%. Details →First reported promptarmor.com
Connecting AI agents to outside services explodes the risk radius
The Register reports on PromptArmor research finding that AI agent connectors — OpenAI/ChatGPT and Anthropic/Claude MCP-based integrations with services like Gmail, Slack, and Dropbox — change constantly, with 931 of 2,517 connectors (37%) changing over six weeks, 1,686 new tools added and 1,127 tool descriptions rewritten. The study found connectors gaining write and destructive capabilities (Dropbox went from 8 to 24 tools, 0 to 4 destructive), permission scopes shifting, injected model instructions appearing, and about 2 in 5 Claude connectors likely calling additional external AI services. Details →First reported · updated · 2 reports arxiv.org
Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks
A security research paper, 'Capability Gates Are Not Authorization,' audits LangChain/LangGraph, LlamaIndex, and the Stripe Agent Toolkit and finds that all three provide capability gating by default but none enforce a deterministic fail-closed per-call value authorization gate, enabling classic confused-deputy abuse where an attacker-influenced model emits an unauthorized side-effecting call (e.g., a payout). The authors introduce SCOPEGATE, a five-stage PDP/PEP control (scope, authorization, money ceiling, idempotency, default deny), and report an identical unauthorized payout executing under LangChain's default dispatch but denied by SCOPEGATE, with an available artifact (github.com/raceksd-source/scopegate-runtime). Details →First reported · updated · 4 reports arxiv.org
Agent Data Injection Attacks are Realistic Threats to AI Agents
A research paper by Woohyuk Choi and colleagues introduces agent data injection attacks (ADI), a new category of indirect prompt injection in which malicious data is disguised as trusted data (such as security-critical metadata or agent context data like tool call/response formats) rather than as instructions. The authors demonstrate ADI against real-world agents, achieving arbitrary click attacks on web agents (Claude in Chrome, Antigravity, Nanobrowser) and remote code execution plus supply-chain attacks on coding agents (Claude Code, Codex, Gemini CLI), showing it bypasses existing IPI defenses because agents fail to isolate trusted from untrusted data. Details →First reported acm.org
Confused Deputy Attack Against Model Context Protocol | ACM Transactions on Software Engineering and Methodology
An ACM TOSEM paper uncovers the "confused deputy attack" against the Model Context Protocol (MCP), where an adversarial server with subtly manipulated metadata overshadows a benign server and intercepts tool invocations without overt malicious behavior. The authors built Puppet, an automated evaluation framework that rewrites benign tool descriptions to hijack tool selection, achieving hijacking rates up to 90.89% and payload execution up to 86.46% across 14 models, while evading MCP-Scan and McpSafetyScanner which cannot detect metadata-level manipulation. Details →First reported arxiv.org
BraveGuard: From Open-World Threats to Safer Computer-Use Agents
BraveGuard is a self-evolving defense framework, presented in an arXiv paper (arXiv:2606.01166), for training guard models to monitor computer-use agents that interact with files, terminals, browsers, and external tools. The framework mines open-world threat signals, instantiates them as executable agent tasks, and derives trajectory-level supervision; on the AgentHazard benchmark it raised detection accuracy from 38.79% to 82.38% over off-the-shelf guards like Qwen3-Guard and Llama-Guard variants. Details →First reported adversa.ai
Solving GitHub’s Secure Code game with an AI red teaming agent
Adversa AI documents pointing its autonomous AI Red Teaming Agent at GitHub's open-source Secure Code Game 'ProdBot' challenge, an intentionally vulnerable agentic-AI teaching target where the goal is to coax the agent into leaking a flag stored one directory above its sandbox. The agent cleared the first three levels (Sandbox, Web, MCP) at full score in a single 57-second run, relying on 'context seeding' — fabricating a plausible prior workflow implying authentication had already occurred — rather than overt jailbreak language, illustrating that each added capability (web browsing, MCP tool chains, skills, multi-agent) opens a new attack surface via indirect injection, poisoned tool chains, and confused-deputy trust. Details →First reported aclanthology.org
Data Exfiltration via Backdoored Tool Use
Researchers present Back-Reveal, a data exfiltration attack that embeds semantic triggers into fine-tuned tool-use LLM agents; when triggered, the backdoored agent invokes memory-access tool calls to retrieve stored user context and covertly transmits it to an attacker-controlled server disguised as legitimate retrieval/search API requests. The paper shows multi-turn interaction amplifies leakage, as attacker-controlled retrieval responses steer subsequent agent behavior to induce cumulative disclosure, and evaluates defenses like NeMo Guardrails and LLM Guard as targets. Details →First reported arxiv.org
An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios
A joint evaluation by the Singapore AI Safety Institute and the Korea AI Safety Institute (arXiv:2606.17114) tested three tool-using LLM agents across 12 realistic, non-adversarial tasks spanning customer support, DevOps, web automation, and productivity, measuring five data-handling risk types. None of the agents achieved fully correct and fully safe execution, with successful task completion often coinciding with data-handling failures such as accessing unnecessary information or disclosing data to inappropriate recipients. Details →First reported akamai.com
AI Reconnaissance: The Missing Layer in Chatbot Security
Akamai's AI security research team describes "AI reconnaissance," a technique in which deployed AI assistants block direct requests for sensitive data but readily leak operational context — capabilities, boundaries, knowledge-base access, and tool/action surface — when asked simple, benign-looking questions. This leaked context lets attackers craft targeted, application-specific prompt injections that bypass model-level safety, and the authors argue runtime guardrails must detect this early probing stage of the attack kill chain. Details →First reported arxiv.org
Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability
The paper "Rethinking MCP Security" presents MCPZoo, the largest collection of runtime Model Context Protocol (MCP) servers for dynamic analysis (64,611 unique servers, 37,288+ supporting dynamic analysis), built via a multi-agent framework that transforms static repositories into runnable services. Using it, the authors conduct an ecosystem-scale measurement showing that while existing MCP security scanners flag 96.89% of servers as risky, manual validation finds under 50% of sampled alerts are true positives, with inconsistent outputs across scanners. Details →First reported arxiv.org
The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
A systematization-of-knowledge paper by Mohammadreza Rashidi organizes 39 works (2023-2026) on execution security for AI coding agents into 17 categories covering sandbox isolation, capability/access control, policy enforcement, TOCTOU races, and MCP threats, and verifies four disclosed, patched CVEs affecting production agent harnesses. It surfaces five cross-cutting gaps, including denylist policy failure rates of 69-98% and benign out-of-scope agent actions occurring at rates up to 17.1% under realistic prompting. Details →First reported phoenix.security
Supply Chain Attacks 2026: npm, PyPI, VS Code, AI Agents — 0 CVEs
Phoenix Security's Malware Package Intelligence report analyzes 59 supply chain attack campaigns and 657 malicious package-versions from June 2024 to June 2026, documenting an acceleration across npm, PyPI, and the VS Code Marketplace. It highlights a May 2026 self-propagating worm that turned one compromised maintainer token into 226 poisoned packages, and finds AI agent tooling — MCP server injection, .cursorrules poisoning, CLAUDE.md hidden instructions, and AI coding assistant SessionStart hooks — used as a confirmed delivery mechanism in at least 14 of the 59 campaigns. Details →First reported aminrj.com
Deleting the Malicious MCP Server Doesn't Save You | Amine Raji, PhD
A lab-built demonstration (mcp-attack-labs, Lab 08) chains MCP tool-description poisoning into an Agent-to-Agent (A2A) intrusion: a poisoned tool instructs the compromised agent to register a rogue A2A agent, hijack routing via shadowing, exfiltrate data, and persist even after the malicious MCP server is deleted. Each stage maps to a named vulnerability class (OWASP MCP Top 10 MCP03, ASI10, ASI07) and is paired with a detection that fires on it. Details →First reported · updated · 2 reports canopii.dev
State of MCP Security 2026 v2
Canopii's "State of MCP Security 2026" report scanned more than 11,000 published Model Context Protocol servers and found 830 graded D or F, 232 with confirmed dangerous code sinks (eval, shell injection, unsafe deserialization) that can turn prompt injection into host code execution, 184 versions that silently altered tool definitions after publication ("rug pulls"), 1,617 servers shipping dependencies with known vulnerabilities, 260 running install-time scripts, and 7 confirmed typosquats. Popular servers with 1,000+ GitHub stars were over 5x more likely to be high-risk. Details →First reported mcpexplorer.com
We Ran a Live Handshake Against 995 MCP Servers. Only 39 Are Verified.
MCPExplorer ran a live MCP `tools/list` handshake against all 995 servers in its index on July 4, 2026; only 277 answered and just 39 met their verification bar. The scan found 59% of reachable servers expose at least one write tool, 67 expose destructive tools (e.g. `delete_instance`, `revoke_api_key`, `wipeLogs`), 467 have unknown provenance, and 30 different servers expose an identically-named `search` tool creating tool-name collision risks for agents. Details →First reported air.security
The Story of Skills - How We Hijacked 26,000 Agents With One Instagram Ad
Security firm AIR demonstrated that a malicious AI agent 'skill' (brand-landingpage) could pass current skill security scanners and reach over 26,000 users via an open-source agents repository and Instagram ads. The skill directed agents to install a fake Stitch SDK from an attacker-controlled domain (stitch-design.ai) that redirected to the real Google Stitch site, evading static SKILL.md analysis and later allowing payload changes. 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 snyk.io
What nearly 10,000 developer environments reveal about agentic development risk
Snyk analyzed nearly 10,000 developer environments to examine risks introduced by AI coding agents as a new layer in the software supply chain, highlighting issues around tools, instructions, and permissions in agentic development. Details →First reported lateos.ai
GPT-5 Nano IPI Assessment — LLM Vulnerability Research
A black-box prompt injection susceptibility assessment of GPT-5 Nano using the IPI Taxonomy v0.13 across 201 analyzed test cases, reporting a 38.3% overall susceptibility rate. The model was fully resistant to surface-level attacks (CSS concealment, HTML cloaking, SEO phishing, RAG corpus poisoning) but highly vulnerable to recursive instruction framing (100%) and MCP tool description poisoning (80%). Details →First reported trendmicro.com
Pwning Agentic AI Part I: Your AI Agent Is Already Compromised | Trend Micro (US)
Trend Micro's TrendAI Research describes a new agentic-AI exploitation pattern they call return-to-tool (RTT) exploits, where embedded instructions in benign-looking untrusted input cause an AI agent to invoke its authorized tools to perform attacker-intended actions such as exfiltrating production database credentials. The research notes a vulnerable PostgreSQL MCP server image pulled over 100,000 times from Docker Hub as a realistic exposure vector. 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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