First reported cloudsecuritywire.com
Lead dispatch
First reported · updated · 2 reports island.io
AgentBaiting: How Fake AI Skills Deliver Malware at Scale
The FakeGit campaign, detailed by Island security researcher Oleg Zaytsev, uses roughly 7,600 malicious GitHub repositories—over 800 posing as AI Skills or MCP servers—to deliver SmartLoader malware, which establishes persistence and installs the StealC information stealer. Researchers coined the technique 'AgentBaiting,' where AI agents like Claude Code, Gemini, and ChatGPT autonomously discover the attacker repositories, treat the malicious READMEs as legitimate documentation, and hand installation instructions to users; the operation recorded over 14 million downloads and peaked in April 2026.supply-chain · tool-abuse · malware-distribution · agent-baiting
mcp · ai-agents · llm · github
Severity
0.78
The wire · latest
First reported openai.com
Continuously hardening ChatGPT Atlas against prompt injection attacks
OpenAI describes how it hardens ChatGPT Atlas's browser agent-mode against prompt injection, using reinforcement-learning-powered automated red teaming to discover novel attack strategies internally before they appear in the wild. The post details a recent security update that shipped a newly adversarially trained model and strengthened safeguards after internal red teaming uncovered a new class of prompt-injection attacks, and outlines a rapid response loop for continuously finding and patching agent exploits. Details →First reported · updated · 2 reports manifold.security
Microsoft Azure DevOps MCP Flaw Lets Hidden PR Comments Hijack AI Review Agents
Manifold Security disclosed a confused-deputy vulnerability in Microsoft's official Azure DevOps MCP server where a pull request description tool returned PR text without the prompt-injection guardrail applied to other tools. An attacker can embed an invisible HTML comment in a PR description that renders as nothing in the web UI but is passed verbatim to a reviewer's AI coding agent, hijacking it to access projects the attacker cannot reach and exfiltrate what it finds. Microsoft addressed the issue in a subsequent release (v2.8.0). Details →First reported ruxu.dev
Build a Basic AI Agent From Scratch: Security II
"Build a Basic AI Agent From Scratch: Security II" is a hands-on tutorial that walks through hardening an agent harness with a Docker execution sandbox, prompt-injection defenses that stop the model from treating tool output as instructions, JSON-Schema input validation, resource limits, secret management, audit logging, and kill switches. The article outlines a six-section threat model and maps each defensive control to a dedicated module in an accompanying open-source repo. Details →First reported embracethered.com
AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection
AWS Kiro, an agentic coding IDE, was vulnerable to arbitrary command execution via indirect prompt injection: hidden text on a web page (or a comment in a source file) processed by the agent could make Kiro use its no-approval fsWrite tool to rewrite ~/.kiro/settings/mcp.json (or allowlist all Bash commands in .vscode/settings.json), causing it to launch attacker-specified MCP servers/commands and achieve RCE on the developer's machine, bypassing the human 'allow' approval boundary. Discovered by Intezer with Kodem Security and independently by Embrace The Red (Johann Rehberger); AWS has patched the issue. Details →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 mitiga.io
Modern Malware — Spyware Skills, Hijacked Base URLs, and 1,230+ Leaking API Keys in AI Instruction Files
Mitiga Labs details malware hidden in AI agent instruction files — Cursor rules, Anthropic Skills, Claude Hooks, AGENTS.md/CLAUDE.md context files, MCP server configs, and .pyc droppers — that AI agents follow with near-zero validation. The research found prompt-exfiltration tradecraft caught in the wild, attacker-controlled ANTHROPIC_BASE_URL overrides routing Claude traffic through MITM proxies, permission-bypass defaults, and over 1,230 hardcoded API keys and JWT tokens across tens of services. Mitiga also released a free scanner, Skillgate, built during the investigation. Details →First reported · updated · 4 reports zscaler.com
Indirect Prompt Injection Targets AI Agents | ThreatLabz
Zscaler ThreatLabz observed two in-the-wild campaigns using indirect prompt injection (IPI) embedded in malicious websites to manipulate web-browsing AI agents. One campaign uses SEO poisoning around a fake Python library (requests-secure-v2) with hidden prompts and schema-markup instructions telling agents to make a cryptocurrency payment to a hardcoded wallet to obtain an API key; the other typosquats the DeFi tracker DeBank, embedding prompts convincing agents the fraudulent site is legitimate. In testing an autonomous payment-capable agent across 26 LLMs, four (Llama 3.3 70B, Llama 3.2 90B Vision, Gemini 3 Flash, Gemini 2.5 Pro) were tricked into paying and two miscategorized the fake DeBank as trusted. Details →First reported youtube.com
CyberTalks: Data Poisoning Attacks on ML & Agentic AI Systems | Jason Ross |COASP
A recorded EC-Council CyberTalks webinar by Salesforce Product Security Principal Jason Ross covers data poisoning attacks against machine learning and agentic AI systems, walking through the ML lifecycle attack surface, AI supply-chain risks (including Hugging Face attacks and sleeper-agent backdoors), RAG embedding-database poisoning, cascading poisoning, and MCP exploitation examples such as GitHub and WhatsApp MCP abuse. The session also outlines mitigation strategies including secure data sourcing, validation, monitoring, and governance frameworks. Details →First reported arxiv.org
Securing the AI Agent: A Unified Framework for Multi-Layer Agent Red Teaming
Tencent's Zhuque Lab released AI-Infra-Guard, an open-source multi-layer AI agent red-teaming framework, on June 30, 2026, described in an arXiv paper and published to GitHub. The framework matches a detection paradigm to each layer of an agent's attack surface: deterministic rule matching over 75+ components and 1,400+ vulnerability rules, LLM-driven agentic auditing of MCP servers and agent-skill packages (supply-chain auditing), multi-turn black-box agent red teaming, and a jailbreak harness with 26+ attack operators across sixteen datasets. Details →First reported firetail.ai
LLM08: Vector & Embedding Weaknesses - FireTail blog posts
FireTail's blog explains OWASP LLM08: Vector and Embedding Weaknesses, covering risks such as unauthorized access, cross-context information leaks, embedding inversion attacks, data poisoning, and behavior alteration in RAG systems that use vector databases. The post lists mitigation techniques including access control, data validation, source authentication, and monitoring. Details →First reported arxiv.org
KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems
KidnapRAG is a research paper presenting a black-box poisoning attack against Agentic RAG systems in which the attacker only publishes externally retrievable poisoned documents. The method uses three role-specific documents (Bait, Chain-Link, and Mal-Ins) to hijack an agent's multi-step reasoning chain, outperforming existing poisoning baselines across multiple frameworks, LLM backbones, and benchmarks; code is released on GitHub. Details →First reported deepinspect.ai
MCP Server Supply Chain Security: The Install Path Nobody Reviews
A DeepInspect analysis lays out five review gates for securing the MCP server supply chain, arguing that adding a third-party MCP server grants code execution, credential access, and text injection with far less scrutiny than an npm dependency. It cites CSA/OX Security research finding 9 of 11 MCP marketplaces affected by a STDIO-interface design flaw, 40+ MCP CVEs in early 2026 (including CVE-2026-33032 in nginx-ui MCP and CVE-2026-0755 in gemini-mcp-tool, both CVSS 9.8), and details tool-description poisoning as indirect prompt injection (MITRE ATLAS AML.T0051.001). 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 medium.com
Prompt Injection: The XSS of LLMs | Tomasus
Tomasus publishes an explainer framing prompt injection as "the XSS of LLMs," arguing that because a model reads instructions and data as one continuous token stream, the system/user prompt boundary is only a convention and later persuasive text can override the developer's rules. The piece walks through direct injection (override, role-play framing, payload smuggling) and indirect injection via retrieved content such as web pages, PDFs, and emails, referencing the OWASP LLM Top 10 and the Greshake et al. indirect prompt injection research. Details →First reported arxiv.org
Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses
Researchers at Penn State introduce FARMA (Forged Amplifying Rationale Memory Attack), which poisons an LLM agent's remembered reasoning history rather than its factual knowledge, inserting forged reasoning traces with evasive language that bypass keyword filters and self-referential reinforcement that defeats consensus-based defenses like A-MemGuard, achieving up to 100% attack success. They also present SENTINEL, a layered defense pipeline whose Reasoning Guard structurally analyzes entries using five weighted signals, reducing FARMA's success rate to as low as 0% with no false positives across 326 benign traces. Details →First reported · updated · 2 reports kodemsecurity.com
LLM Security Testing: OWASP Top 10 Guide 2026
Openlayer's guide summarizes the 2025 OWASP Top 10 for LLM applications, explaining why LLM systems need dedicated security testing and detailing risk categories such as prompt injection, excessive agency, system prompt leakage, poisoned vector stores, unbounded consumption, and vector/embedding weaknesses. It argues that static code analysis and CVE scanning miss inference-time attacks and that agentic systems require session-level testing, while mapping OWASP results to EU AI Act obligations and promoting Openlayer's coverage. Details →First reported pillar.security
The Week of Sandbox Escapes
Pillar Security researchers demonstrated seven sandbox-escape techniques against four AI coding agents — Cursor, OpenAI's Codex, Google's Gemini CLI and Antigravity — where a sandboxed agent writes workspace files that trusted tools running outside the sandbox (Python extensions, Git integrations, hook engines, Docker daemons) later execute. Prompt injection planted in a README, issue, dependency, or diff triggers local command execution on the developer's machine; one Cursor flaw is tracked as CVE-2026-48124 and fixed in version 3.0.0, with most issues patched and vendor-acknowledged. Details →First reported · updated · 2 reports asset-group.github.io
We put the exploit in a picture. Your AI code reviewer never opens it.
Researchers from the University of Missouri-Kansas City's ASSET Research Group demonstrated 'Ghostcommit,' an attack that hides malicious prompt-injection instructions inside a PNG image so AI code reviewers (CodeRabbit, Cursor Bugbot) never see them. A benign-looking AGENTS.md convention file points to build-spec.png, whose rendered text instructs a coding agent to read the repo's .env byte-by-byte and emit the secrets as an integer tuple; the payload sits dormant until an unrelated agent session triggers exfiltration. A proof-of-concept is published on GitHub and the findings were disclosed to affected vendors. Details →First reported keycard.ai
The Agent Security Stack: Transport, Identity, Policy, Runtime
A Keycard explainer maps the "agent security stack" into distinct layers — transport (MCP/OAuth 2.1 authorization), identity, policy, and runtime guardrails that watch for prompt injection — arguing that agent security cannot be collapsed into a single control surface. The piece frames how multi-agent call chains multiply control surfaces (LLM tool invocation, transport, credential, authorization) and argues identity/authorization is the most under-served layer, citing recent CrowdStrike/SGNL and Palo Alto/CyberArk acquisitions. Details →First reported · updated · 3 reports appsentinels.ai
One Poisoned MCP Server Can Hijack All the Others — Coograph
MCP tool poisoning hides malicious instructions inside a Model Context Protocol server's tool descriptions, parameter schemas, or return values — invisible to human reviewers but fully read by the LLM, which then selects and executes the poisoned tool, enabling data exfiltration, credential theft, or lateral movement. The technique spans schema poisoning, tool shadowing, and rug pulls, and is catalogued as OWASP MCP03:2025; a single malicious server can influence an agent's decisions across all connected tools (cross-tool poisoning). Details →First reported · updated · 15 reports everydayonai.com
What Is a Prompt Injection Attack? Types, Examples, and Defenses | AI EdgeLabs
An educational reference explainer, "What Is a Prompt Injection Attack? Types, Examples, and Defenses," catalogs prompt injection technique classes—including direct, indirect, and multimodal (hidden image/document) injection—and maps them to OWASP LLM01:2025, NIST AI 600-1, and mitigation guidance such as tool gating, source trust, and audit logs. The piece synthesizes existing research and known incidents (e.g., EchoLeak, the lethal trifecta) rather than presenting a specific new event or finding. Details →First reported asana.com
Breaking the Lethal Trifecta: How Asana Thinks About Agentic AI Security • Asana
Asana engineering explains how it approaches agentic AI security using Simon Willison's "lethal trifecta" framework — the convergence of access to sensitive data, exposure to untrusted content, and the ability to externally communicate (create side effects) that together enable prompt-injection attacks. The piece argues that since prompt injection cannot be reliably solved, defenders should break at least one leg of the trifecta, citing demonstrated attacks against Microsoft 365 Copilot (EchoLeak), GitHub's MCP server, and Slack AI. 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 medium.com
Prompt Injection Is Just SSRF for Text | MCP Security → Part 3 | by Abhishek meena | Jul, 2026
Part 3 of an MCP bug bounty guide by Abhishek meena frames MCP prompt injection as analogous to SSRF: tool outputs (URLs, files, emails, API responses) are attacker-controlled text that the model reads and treats as instructions. The write-up explains how to find, exploit, and argue indirect prompt injection via tool output, with sanitized PoCs referenced. Details →First reported arxiv.org
Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems
"Bad Memory" is a research paper studying prompt injection attacks in memory-based agentic systems using a sandboxed synthetic workspace, evaluating Anthropic Claude Code and OpenAI Codex across four models. The authors find that while it is hard to make an agent overwrite its own memory files using untrusted external content, payloads already planted in persistent memory files can successfully compromise current and future sessions, with attack success and persistence varying by system, model, and adversarial goal. Details →First reported · updated · 2 reports openai.com
GPT-Red: Unlocking Self-Improvement for Robustness
OpenAI describes GPT-Red, an internal automated red-teaming model trained at large post-training compute scale to discover prompt injection vulnerabilities in its models and generate adversarial training data. OpenAI reports using GPT-Red to adversarially train GPT-5.6 Sol, claiming 6x fewer failures on its hardest direct prompt injection benchmark versus a prior production model. 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 · 5 reports varonis.com
SearchLeak: How We Turned M365 Copilot Into a One-Click Data Exfiltration Weapon
SearchLeak (CVE-2026-42824) is a critical three-stage vulnerability chain in Microsoft 365 Copilot Enterprise discovered by Varonis Threat Labs that lets an attacker steal MFA codes, emails, meeting details, and organizational files with a single click on a trusted microsoft.com link. It chains a Parameter-to-Prompt (P2P) injection via the search q parameter with an HTML rendering race condition and a CSP bypass through Bing's allowlisted image-search SSRF endpoint to silently exfiltrate a victim's mailbox, calendar, SharePoint, and OneDrive data. Microsoft remediated the flaw and rated it critical. Details →First reported swarmnetics.com
Agentjacking and MCP trust: are AI coding agents too easy to steer?
Agentjacking, described by Swarmnetics and Tenet Security, abuses trusted error-report inputs in AI coding agents: an attacker with a publicly exposed Sentry DSN can inject malicious instructions into telemetry that the agent processes, steering it to exfiltrate secrets such as cloud keys, Git credentials, and private repo URLs. The root flaw is that these systems treat source trust as if it equals action trust, letting external report text become executable guidance. Details →First reported tenetsecurity.ai
A public Sentry key is all it takes to hijack Claude Code, Cursor, and Codex
Researchers at Tenet Security describe "agentjacking," an attack in which a publicly exposed Sentry key lets an attacker inject fake error messages that AI coding agents such as Claude Code, Cursor, and Codex ingest via the Sentry MCP server. The crafted error content acts as an indirect prompt injection, hijacking the agent to execute attacker-directed actions; the team also published a mitigation tool, agent-jackstop, on GitHub. 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 medium.com
Prompt Injection Is No Longer Just a Chatbot Problem
A Medium explainer titled "Prompt Injection Is No Longer Just a Chatbot Problem" argues that as AI applications gain the ability to search documents, retain memory, call APIs, and run autonomous agent workflows, prompt injection now extends beyond chatbots to poison retrieved knowledge, corrupt long-term memory, manipulate an agent's plan, or trigger unauthorized tool calls. The piece frames prompt injection as a core security challenge across prompts, RAG, tools, memory, and agents. Details →First reported · updated · 5 reports arxiv.org
Prompt Injection as Role Confusion
The paper "Prompt Injection as Role Confusion" (arXiv:2603.12277, ICML 2026) by Charles Ye, Jasmine Cui, and Dylan Hadfield-Menell traces prompt injection to role confusion: LLMs perceive the source of text from how it sounds rather than its labeled role, so injected text occupies the same representational space as the trusted role it imitates. The authors introduce role probes to measure internal role perception and demonstrate CoT Forgery, a zero-shot attack injecting fabricated reasoning into user prompts and tool outputs that yields 60% attack success against frontier models with near-zero baselines. Details →First reported arxiv.org
The Injection Paradox: Brand-Level Suppression in Safety-Trained LLM Recommendations via RAG Context Injection
The paper 'The Injection Paradox' by Hyunseok Paeng documents a reproducible failure mode in RAG-based LLM recommendation systems where indirect prompt injections embedded in retrieved documents backfire, suppressing the injected brand below its injection-free baseline. In safety-trained Claude models (e.g., Claude Opus 4.6), a single injected document drops the target brand from a 54% baseline to zero top-2 recommendations and propagates suppression to the brand's uninjected documents, while GPT models instead show increased recommendations. The authors note this enables a reverse-attack scenario where an adversary injects a competitor's documents to suppress that competitor, and release code, prompts, and results. Details →First reported arxiv.org
Devil in the Lens: Analyzing and Defending Physical Prompt Injection Against Vision-Language Models on Wearable Devices
A research paper, "Devil in the Lens," characterizes physical prompt injection attacks against Vision-Language Models on wearable devices such as AI glasses, where malicious text embedded in real-world environments acts as an indirect prompt-injection channel that hijacks model behavior. Using photos captured from AI glasses across 200+ environments, the authors identify 6 threat vectors, evaluate 12 VLMs (achieving attack success rates up to 96% simulated and 60% real-world), and propose two defenses: a mask-based external filter and a semantic-vector-based internal detector. Details →First reported · updated · 4 reports sentia.community
The Confused Deputy with a Chat Window: Why AI Agents Are Exposing the Security Checks Enterprises Never Wrote – The Sentia AI Community
An explainer from the Sentia AI Community argues that autonomous, write-enabled LLM agents connected to production APIs re-introduce the classic 'confused deputy' problem: because an agent's interface is natural language, it lacks a native, cryptographic way to verify who authorized a given instruction, so untrusted input can drive privileged actions. The piece frames this as a structural gap in enterprise security models built around implicit human judgment and static perimeter API controls. Details →First reported · updated · 3 reports medium.com
Your AI Agent Trusts Every Tool It's Ever Been Introduced To
An analysis piece, 'The MCP paradox,' argues that the Model Context Protocol standardized not only how agents reach tools but also how attackers reach agents, walking through concrete vectors like tool poisoning attacks where a malicious tool description instructs an agent to exfiltrate secrets (e.g. SSH keys) via text the user never sees. The article cites Invariant Labs' April 2025 tool-poisoning proof of concept and MCP's own design choices, and proposes defensive controls to harden MCP servers. Details →First reported · updated · 3 reports acm.org
Benchmarking and Defending against Indirect Prompt Injection Attacks on Large Language Models | Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1
PromptShield is a benchmark introduced in an ACM SIGKDD paper for training and evaluating deployable prompt injection detectors, curated to include both conversational and application-structured data. The authors also fine-tune a new prompt injection detector that achieves higher performance in the low false-positive-rate regime than prior schemes. Details →First reported github.com
GitHub - opena2a-org/damn-vulnerable-ai-agent: Damn Vulnerable AI Agent is a deliberately vulnerable AI agent platform for security testing and education.
Damn Vulnerable AI Agent (DVAA) by opena2a-org is a deliberately vulnerable AI agent platform, distributed as a GitHub repo and Docker image (opena2a/dvaa), built for security testing and education. Modeled after projects like DVWA, it ships a fleet of intentionally exploitable AI agents so practitioners can practice attacks such as prompt injection and tool/agent abuse against a safe target. 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 · updated · 2 reports crowdstrike.com
CrowdStrike Uncovers New Prompt Injection Techniques
CrowdStrike's AI security research team disclosed 18 new additions to its prompt injection taxonomy, expanding coverage to over 200 techniques, and detailed five notably: Trigger-Activated Rule Addition (PT0201), Cognitive Token Suppression (PT0197), Algorithmic Payload Decomposition (PT0200), Special Token Injection (PT0198), and one further method. The techniques target AI agents that crawl webpages, access file stores, and run shell commands, using indirect injection to hide malicious instructions in consumed data. Details →First reported exein.io
Physical AI Security: A Threat Model for Edge Devices
Exein's blog post "Physical AI Security: A Threat Model for Edge Devices" argues that on-device AI (cameras, robots, drones running vision-language models and LLMs locally) introduces risks classic embedded threat models miss: every sensor becomes an instruction channel enabling physical-world prompt injection (e.g. text on a sign in front of a camera), probabilistic behavior that cannot be patched like a CVE, and unattended failures that act on the physical world via actuators. It proposes modeling the agent loop rather than individual components. Details →First reported · updated · 2 reports senthex.com
Prompt Injection Prevention for Coding Agents | Fiddler AI Blog
A Fiddler AI blog post explains prompt injection risks specific to coding agents, where untrusted inbound context (repositories, issues, docs, and tool returns) can turn an injected instruction into executed code or remote code execution. It argues model-level guardrails are bypassable and advocates layered prevention: isolating untrusted input, least-privilege tool and credential scoping, blast-radius containment, and runtime policy enforcement with decision lineage. Details →First reported ivconsulting.in
Prompt Injection: The AI Agent Security Risk for SMBs
An explainer from IV Consulting describes prompt injection as the top OWASP risk for AI and LLM applications, walking through how hidden instructions planted in emails, web pages, documents, or support tickets can trick an AI agent into exfiltrating data from an inbox or CRM. The piece frames the risk for SMBs and promises five practical guardrails to apply before granting agents access to data and actions. Details →First reported sprinklenet.com
Prompt Injection Risk in Enterprise RAG Systems
Sprinklenet analysis explains how indirect prompt injection threatens enterprise RAG deployments, arriving not through the chat box but through poisoned corpus content — vendor PDFs with hidden white-on-white text, phishing-quoted support tickets, or crawled web pages — that competes with the system prompt once retrieval pulls it into context. The piece recommends architectural defenses: isolating retrieved content from instructions, allowlisting tool calls, tiering source trust, filtering outputs, running an injection evaluation suite, and logging prompts and completions. Details →First reported · updated · 3 reports theregister.com
GitLost: How We Tricked GitHub’s AI Agent into Leaking Private Repos - Noma Security
Noma Security's "GitLost" research demonstrated that GitHub Agentic Workflows (AI agents backed by Claude or Copilot) could be tricked via indirect prompt injection: an unauthenticated attacker posts a crafted issue on a public org repo, and the agent fetches a private repo's README and posts it in a public comment. The PoC used an "Additionally" prefix to bypass guardrails and was disclosed to GitHub with no public patch date noted. Details →First reported nhimg.org
AI browser guardrail bypass exposes a new data theft path
LayerX Security describes a "BioShocking" attack that games an AI browser into violating its guardrails by establishing a false reality, enabling data theft, code copying, and system command execution. The attack exploits the browser-mediated trust boundary, using indirect or staged instructions embedded in webpage context that pass prompt filters yet still steer the model into malicious enterprise actions inside an authenticated session. Details →First reported kili-technology.com
LLM Red Teaming in 2026: How Frontier Labs Test AI
Kili Technology's guide explains how frontier labs approach LLM red teaming in 2026, surveying attack surfaces such as multi-turn and many-shot attacks, agentic prompt injection, and multimodal/multilingual surfaces, and argues that public adversarial benchmarks are losing reliability in favor of private, expert-built adversarial datasets. The piece also covers enterprise red-teaming requirements and the regulatory bar (e.g. EU AI Act) that deployers must meet. Details →How the wire is made
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