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 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 aclanthology.org
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Knowledge Poisoning Attacks
MM-PoisonRAG is a research paper presenting local and global knowledge poisoning attacks against multimodal retrieval-augmented generation (RAG) systems used by multimodal large language models (MLLMs). The work shows how injecting poisoned entries into the retrieval knowledge base can manipulate MLLM outputs, exploiting RAG's reliance on retrieved external content. 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 · updated · 2 reports medium.com
RAG Security Guide: Prevent Enterprise AI Data Leakage
A vendor guide from LangProtect explains how Retrieval-Augmented Generation (RAG) systems can leak sensitive enterprise data, using an illustrative case of a healthcare AI assistant that returned document excerpts across business units because a shared vector index was queried before document permissions were evaluated. The article outlines the mechanism of RAG data leakage and references academic work on adaptively attacking RAG systems to exfiltrate private knowledge bases. 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 · 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 · 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 helpnetsecurity.com
99.9% of fixable AI vulnerabilities remain unpatched
Orca Security's 2026 State of AI Security Report, summarized by Help Net Security, finds 81.2% of companies running AI packages have at least one known vulnerability and 99.9% of AI vulnerability alerts with an available fix remain unpatched. The report describes attackers moving across five layers of the AI stack — package registries, model hubs, developer tools, agent frameworks, and brand trust — while organizations deploy agents, RAG pipelines, and vector databases with weak security hygiene. 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 petri.com
Copilot Didn’t Overshare Your Data. Your Permissions Did
Amy Babinchak argues that Microsoft 365 Copilot's tendency to surface confidential documents, emails, and SharePoint content is not a Copilot bug but a reflection of pre-existing permission sprawl — EEEU groups, broken inheritance, and unexpired sharing links — that plain-language prompts now make instantly discoverable. Citing Concentric AI research that 16% of business-critical data in the average tenant is overshared, the piece recommends restricting Copilot discovery from high-risk areas and using Purview and SharePoint Advanced Management to remediate. Details →First reported substack.com
Data Exfiltration from Slack AI via indirect prompt injection
PromptArmor identified an indirect prompt injection vulnerability in Slack AI, a RAG-style chat search interface, where an attacker seeds poisoned tokens into a public channel or an imported document. When a user later queries Slack AI, the injected instructions cause private data (e.g. an API key from a private channel) to be embedded into a malicious Markdown link that exfiltrates the secret to the attacker's server when clicked. 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 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 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 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 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 medium.com
Your AI Agent Will Leak Your Secrets One Letter at a Time | by Sebastian Buzdugan | Jul, 2026
An explainer by Sebastian Buzdugan describes how AI agents can be tricked into exfiltrating a user's private context-window data — names, employers, hometowns — one letter at a time via outbound URL requests, while the visible reply appears benign. The piece frames the risk around Simon Willison's 'lethal trifecta': an agent with access to private data, exposure to untrusted content, and the ability to communicate externally. 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 netwrix.com
What is AI jailbreaking?
Netwrix's cybersecurity glossary entry defines AI jailbreaking as the practice of bypassing safeguards, restrictions, and safety controls built into AI models, and explains techniques including prompt injection, role-playing attacks, indirect prompt injection via external content, gradual context manipulation, and infrastructure-level tampering. Details →First reported bugcrowd.com
One-Click Data Exfiltration via rovoChatPrompt URL Parameter (Confluence / Rovo) - CrowdStream
A disclosed and now-patched vulnerability in Atlassian Rovo, the default AI assistant across Confluence and other Atlassian products, allowed the `rovoChatPrompt` URL parameter to preload an arbitrary prompt into a victim's Rovo chat. When an authenticated user clicked a crafted link, Rovo executed the embedded prompt as a genuine query, using indirect task-framed language ('help me identify this image') to bypass guardrails and exfiltrate Confluence pages, secrets, and connected-app data (Jira, SharePoint, Outlook) to an attacker host via an image-fetch URL. Atlassian remediated the issue server-side and the reporter validated the fix. Details →First reported wraith.sh
Data Exfiltration via Markdown Images: The Quiet AI Vulnerability
Wraith's attack guide by Anthony D'Onofrio details data exfiltration via markdown image rendering in AI products, where an injected payload (via prompt injection, RAG doc, shared document, or email) causes an LLM to emit a markdown image whose URL embeds secrets like system prompts, conversation history, or API keys; when the chat client renders the markdown, the browser silently fetches the attacker's URL, leaking the data with zero clicks and no visible artifact. The guide notes variants have hit ChatGPT, Microsoft Copilot, GitHub Copilot Chat, Slack AI Assistant, Google Bard, and Claude.ai, and covers four defensive patterns. Details →First reported arxiv.org
Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems
CareAttack is a model-centric attack framework that edits the parameters of open-source dense retrievers (Qwen3-Embedding-0.6B and BGE-M3) to promote malicious passages into the top-k retrieved evidence of RAG systems, bypassing detectable corpus-manipulation approaches. Evaluated on three benchmark datasets, it uses conflict-aware retriever editing and attack-preserving anchor repair to inject malicious knowledge for batches of target prompts while preserving behavior on non-target prompts; code is published. Details →First reported crunchtools.com
The Prompt Injection That Copies Itself
Crunchtools publishes an explainer on prompt injection against AI agents, arguing that the quietest danger is self-replicating injection — citing the Morris II research worm (Cornell Tech and Technion, 2024) that embedded an adversarial prompt in an email, hijacked assistants across ChatGPT, Gemini, and LLaVA to leak data, and forwarded itself with no human clicks. The piece also references a Replit coding agent deleting a production database and the Pliny the Prompter jailbreak community, and mentions the author's defensive project 'Trentina' built to catch injection. Details →First reported lakera.ai
Memory poisoning in AI agents: are your controls keeping up?
An NHIMG editorial based on Lakera research discusses memory poisoning and long-horizon goal hijacks in AI agents, where attackers persistently alter what agents remember and optimize for, creating silent compromise paths that unfold across sessions. The piece offers governance guidance such as treating persistent memory as governed state, tracing provenance on memory items, and monitoring multi-step workflows for objective drift. Details →First reported acm.org
When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse | Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval
A SIGIR 2026 paper introduces D-SCAN (Document-level Signal Collapse Analysis), a lightweight detection framework that identifies RAG poisoning attacks by monitoring 'Attention Collapse' — a drop in attention entropy as the generator concentrates on injected adversarial documents. The authors show that output-side signals like perplexity fail because poisoned outputs can exhibit false confidence with even lower perplexity than benign ones, and that D-SCAN detects attacks even when they do not alter the final answer. Code is available at github.com/yingtaoren/D-Scan. 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
A Layered Security Framework Against Prompt Injection in RAG-Based Chatbots
A research paper by Gulshan Saleem and colleagues presents a three-layer middleware framework to defend RAG-based chatbots against both direct and indirect prompt injection, combining input screening, a provenance-based instruction hierarchy during context assembly, and output auditing. Evaluated on 5,080 samples across GPT-4o, Llama 3, and Mistral 7B, the framework reduced Attack Success Rate from 71.4% to 11.3% while maintaining a 4.8% false positive rate and 61.2 ms median latency overhead. Details →First reported koreshield.ai
Reproducing an Indirect Prompt Injection Against a RAG Pipeline
A Koreshield write-up reproduces an indirect prompt injection against a RAG customer-support pipeline, where a poisoned knowledge-base document (doc-002) contains a bracketed fake 'system note' instructing the model to dump all retrieved documents and exfiltrate internal tokens with an 'EXFIL::' marker while hiding the action. The author provides runnable code, a public GitHub harness (UncleTeslim/prompt-injection-repro), and real model logs demonstrating the attack against a local Llama3-8B model. Details →First reported agentsafelabs.com
Prompt Injection Is Not a Chatbot Problem: How the Attack Surface Changes When Your LLM Has Tools
An explainer arguing that chatbot-era prompt injection defenses (input filtering, output monitoring, instruction hierarchy, adversarial training) fail against agentic systems because injection enters through tools, retrieved documents, memory, API responses, and inter-agent instructions rather than the user message. It frames the issue as a system-architecture and trust-boundary problem rather than a model-tuning one. 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 elborai.me
Polymarket annotation injection
The author found injected annotations on a Polymarket event page that are rendered server-side and therefore visible to LLMs via web_search even when hidden in the browser. A planted annotation (source 'grok') contained a fake emergency-rate-cut message directing users to withdraw funds at a phishing-style domain, representing an indirect prompt-injection vector through Polymarket's annotation API endpoints. Claude's web search saw the content but correctly flagged it as phishing. Details →First reported stackbuilders.com
Stack Builders - When Text Becomes Code: Securing LLM–Database Integrations
A technical guide based on a Quito Lambda talk demonstrating how prompt injection (direct, indirect, and confused-deputy/exfiltration) can compromise LLM applications that generate SQL over a live Postgres database, using an example LLM-powered SQL analyst with a Streamlit frontend. It walks through layered defenses and what they stop or fail to stop. Details →How the wire is made
Poll & cluster
Internet is crawled for AI security news and near-duplicate coverage is embedded and grouped into durable items.
Curate
AI Agent filters for agentic-AI relevance, classifies and tags each item, scores severity for threats, and writes the summary.
Every item here is one machine-curated intelligence object, not a headline.
Read the wire for free. There is a small charge to ask the index questions.
The wire, open
The complete curated feed, no key required.
- GET /feed.xml — RSS 2.0, every item
- GET /api/items — read-only
The vector desk
Query the index by meaning, not just keyword.
- GET /api/items?tags=&minSeverity=&itemType=
- GET /api/search?q= — keyword
- GET /api/semantic?q= — vector