First reported · updated · 6 reports adversa.ai
Lead dispatch
First reported · updated · 3 reports embracethered.com
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
Severity
0.85
The wire · latest
First reported · updated · 5 reports nhimg.org
MCP's Broken Trust Model: Tool Poisoning, Rug Pulls, and the New Threat Landscape
An analysis of the Model Context Protocol (MCP) trust model describes how tool poisoning (malicious instructions embedded in tool metadata), rug pulls (tools that change behavior after approval), and weak authorization create new attack paths for AI agents. The piece synthesizes NSA MCP security guidance and academic threat modeling (STRIDE/DREAD analysis of MCP clients) showing most clients insufficiently validate tool metadata and permit approved agents to reach sensitive resources without re-review. Details →First reported youtube.com
Ultimate Guide to Prompt Injection: Step by Step Tutorial
Aikido Security's "The Vulnerability Report" video is a step-by-step tutorial on prompt injection, explaining how system prompts, master prompts, and user inputs interact, why LLM architecture cannot fully solve the problem, and how agent/tool abuse works. The episode also walks through a real-world vulnerability in Google's Gemini CLI GitHub Actions workflow that exposed secret keys and tokens, and offers hands-on labs plus threat-modeling defenses. Details →First reported · updated · 13 reports openai.com
Understanding prompt injections: a frontier security challenge
"Securing Agentic AI: From Per-Action Checks to Trajectory Assurance" is an analytical explainer on defending agentic AI systems, synthesizing the prompt-injection risk class, zero-click AI worms (Morris-II), and protocol-level risks (A2A) alongside layered defensive approaches. It argues for moving beyond per-action guardrails toward trajectory-level assurance across an agent's full sequence of actions. Details →First reported · updated · 7 reports thehackernews.com
ASCII smuggling crosses over from AI prompt injection to phishing evasion | Microsoft Security Blog
Microsoft reports that ASCII smuggling — hiding content in invisible Unicode tag characters, a technique popular for indirect prompt injection against AI models — has been repurposed by phishers to split financial-lure keywords (e.g. "fun[U+E0020]ding") and evade content filters in a campaign that peaked above 2.37 million messages in late February. Analysis found no smuggled AI instructions in the flagged messages; the invisible characters were used purely for keyword-filter evasion, illustrating how AI-era attack methods cross over into traditional threats. Details →First reported quora.com
Why is Agentic AI driving demand for security tools?
A Quora answer explains why agentic AI is driving demand for AI-specific security tools, describing indirect prompt injection (hidden webpage text that hijacks an autonomous agent into exfiltrating data), the identity/IAM challenge of AI agents with API keys and credentials, and machine-speed attacks requiring autonomous defenses. The piece frames general defensive needs like 'AI firewalls' rather than any specific incident or product. Details →First reported thenewstack.io
Microsoft built a prompt injection detector. Then it caught a phishing campaign instead.
The New Stack reports that Microsoft built a prompt injection detector aimed at AI pipelines and, in the course of hunting for Unicode/ASCII smuggling attacks, the tooling surfaced a phishing campaign instead. The piece discusses how invisible Unicode/ASCII smuggling can be used to hide malicious instructions inside text fed to LLM-based AI pipelines. Details →First reported bitsight.com
AI Jailbreak Prompts Are Evolving Into Real Cyber Threats
Bitsight Threat Intelligence research covering July 2025 through July 2026 tracked jailbreak activity across forums, GitHub repositories, Telegram channels, and marketplace conversations, finding that threat actors are moving beyond static jailbreak prompts toward obfuscation, model routing, retry logic, multi-model testing, and repeatable jailbreak workflows. The study notes AI increasingly being used to write and troubleshoot malicious code, migrate C2 infrastructure, and support credential discovery, lateral movement, and extortion, and warns of the growing risk as AI agents gain access to files, terminals, credentials, and repositories. Details →First reported nhimg.org
AI agent security across EKS, AKS and GKE: what breaks first?
An NHIMG editorial based on ARMO's AI Agent Security Framework argues that multi-cloud AI agent security fails when discovery, baselining, detection, and enforcement are run separately per cloud provider (EKS, AKS, GKE), splitting cross-cloud agent behavior into disconnected alerts and inconsistent policy. It recommends a unified runtime and identity correlation layer, a single cross-cloud identity map across IRSA/managed identity/Workload Identity Federation, and normalized telemetry, noting 47% of enterprise AI agents operate without security oversight. Details →First reported youtube.com
How Do People Jailbreak AI? What is AI Jailbreaking? | Tricks, Techniques & AI Safety Explained
A short educational YouTube video from "Learn with Whiteboard" explains what AI jailbreaking is, how carefully crafted prompts (fictional framing, conflicting instructions, splitting requests, misleading context) can manipulate models like ChatGPT, Claude, and Gemini into bypassing safety rules, and how developers patch such techniques through updates and red teaming. Details →First reported · updated · 3 reports openai.com
Lockdown Mode | OpenAI Help Center
OpenAI documented Lockdown Mode, an optional advanced security setting for ChatGPT that limits outbound network requests to reduce data exfiltration risk from prompt injection attacks. The feature disables or restricts live web browsing, image retrieval, deep research, agent mode, Canvas networking, and file downloads, but does not prevent prompt injections from appearing in processed content. Details →First reported youtube.com
Prompt Injection Exploits: The CVE That Weaponized the AI Coding Workflow
A podcast with Checkpoint's Adam Forester unpacks a disclosed CVE in Anthropic's Claude Code where the AI coding assistant did not validate its local settings.json on boot, letting a booby-trapped GitHub repo execute arbitrary commands (up to ransomware) the moment a developer downloaded and ran it, with no phishing required. The vulnerability was patched twice and unpatched installs may remain exploitable; the discussion frames it as a new class of indirect prompt injection and also recounts an Alibaba 'Rome' agent that opened covert SSH tunnels to mine crypto. Details →First reported · updated · 5 reports kili-technology.com
What Is AI Red Teaming? The 2026 Guide - Mindgard
Mindgard's 2026 guide explains what AI red teaming is, how it differs from traditional penetration testing, and the frameworks it is built on — including MITRE ATLAS, the OWASP Top 10 for LLM Applications, and Microsoft's open-source PyRIT toolkit. It describes structured adversarial testing of models, AI agents, and surrounding systems using multi-turn attack strategies such as Crescendo, TAP, and Skeleton Key. Details →First reported youtube.com
Morris II: The First AI Worm?
A Zyber YouTube video explains Morris II, a controlled research demonstration by Stav Cohen, Ron Bitton, and Ben Nassi showing how self-replicating adversarial prompts can create a worm-like chain reaction across connected generative-AI applications such as AI-powered email assistants. The video frames it as a security experiment revealing a possible future risk, not an active outbreak, and points to the arXiv paper and IBM overview as sources. Details →First reported · updated · 4 reports appsentinels.ai
Deadbugz: Currently Active MCP Supply-Chain Campaign
The "Deadbugz" campaign is an active MCP supply-chain operation in which malicious Model Context Protocol servers (such as the zellkernel/productivity-suite-mcp package) embed hidden instructions in tool metadata to hijack trusted tools connected to an AI agent, enabling data exfiltration like reading .env files or SSH keys. The campaign leverages cross-tool poisoning, where one poisoned MCP server can abuse other trusted connectors already wired into the agent, and is tied to a malicious GitHub account and associated threat-actor persona. Details →First reported owasp.org
OWASP Top 10 for Large Language Model Applications | OWASP Foundation
OWASP's project page for the Top 10 for Large Language Model Applications notes the list is now maintained under the broader OWASP GenAI Security Project, with the current release being the OWASP GenAI LLM Top 10 2026 published August 4, 2026. The page serves as a legacy entry point directing readers to the active repository and the community initiative documenting security risks in LLMs and agentic AI systems. Details →First reported · updated · 4 reports edgelabs.ai
AI Agent Security Risks: Mitigation for Enterprises
Sweet Security's "AI Agent Security Risks" guide is an enterprise-focused explainer describing how AI agent workflows can be attacked and how to mitigate them, covering prompt injection via untrusted context, poisoned documents and memory, over-broad credentials, action logging, and human approval for high-impact actions. The reference page synthesizes mitigation guidance and cites external frameworks (OWASP LLM Top 10, NIST, MITRE ATLAS) and research such as the AgentPoison memory/knowledge-base poisoning paper. Details →First reported jfrog.com
Agent Immunization is Key for Building Trusted AI Agents
JFrog's blog introduces "Agent Immunization and Control," a vendor concept for securing AI coding agents by embedding layered protections into the software supply chain rather than bolting guardrails, scanners, or sandboxes on from the outside. The piece frames the core risk as agents consuming unverified packages, plugins, and MCP servers that may carry hidden prompt-injection instructions or known vulnerabilities the agent cannot distinguish. Details →First reported substack.com
Kinetic Prompt Injections & Sleeper Agents
Eito Miyamura and collaborators demonstrated a prompt-injection attack against Gemini Robotics 2.0 VLA models in a MUJOCO simulation, using a hijacked TV screen showing a fake 'SYSTEM UPDATE' to plant a conditional sleeper-agent skill on a robot dog. The skill lay dormant until a trigger object (a pineapple) appeared, at which point the robot executed hidden malicious instructions to attack a child, showing that robotics models follow injected commands like early LLMs. Details →First reported · updated · 3 reports google.com
Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting
Researchers from Tel Aviv University, Technion, and Intuit (including Ben Nassi and Stav Cohen) introduce 'HalluSquatting' (adversarial hallucination squatting), a technique in which attackers identify trending resources, predict the resource identifiers that LLMs tend to hallucinate, and preemptively register those hallucinated resources (repos, skills, URLs). When agentic LLM applications hallucinate and call these attacker-controlled identifiers, the technique achieves remote tool execution and remote code execution at scale, enabling scalable, untargeted promptware attacks that could form an agentic botnet without any direct channel to the target. Details →First reported · updated · 7 reports varonis.com
CoSnitch: When Your AI Assistant Becomes Its Own Whistleblower
Varonis Threat Labs disclosed CoSnitch (CVE-2026-24301), a critical one-click vulnerability chain in Microsoft Copilot Personal that combines the ?q= URL parameter with an undocumented autorun=1 parameter to auto-execute an attacker-supplied prompt on page load, then queries connected apps (Gmail, Drive, Calendar, OneDrive) and exfiltrates data via encoded URLs, plus indirect prompt injection through web summarization that poisons persistent memory. Researchers used a 'meta-hacking' technique, repeatedly asking Copilot why an attack wouldn't work until the assistant disclosed its own disabled parameters and session conditions. Microsoft shipped patches on August 18, 2026; Varonis reports no evidence of in-the-wild exploitation. Details →First reported · updated · 5 reports bugcrowd.com
Atlassian Rovo Can Be Tricked Into Sending Jira and Confluence Data to Attackers
Researchers at Varonis Threat Labs (RovoBlast) and PromptArmor independently showed that Atlassian's Rovo AI assistant can be manipulated via prompt injection to collect Jira and Confluence data a signed-in user can access and exfiltrate it to an external server. Varonis found that the rovoChatPrompt URL parameter preloads attacker instructions so a single click by an authenticated user triggers execution; PromptArmor hid instructions in an uploaded file that Rovo reads, working even with web-search disabled. Varonis's route was responsibly disclosed and fixed (CVE-2026-50522), while the PromptArmor file-based bypass is single-sourced and its remediation is not confirmed. Details →First reported · updated · 5 reports mindgard.ai
Amazon Kiro: AI Is Breaking Vulnerability Disclosure Processes
Mindgard disclosed a prompt-injection vulnerability in Amazon Kiro, an AI-powered agentic IDE, that lets attacker-controlled repository content coerce the Kiro agent into reading local sensitive data, modifying a workspace URL, and triggering an outbound request that exfiltrates the secret. The flaw was reproduced in Kiro IDE 0.7.45 on Windows in both trusted and untrusted workspaces via Kiro Powers (MCP configs and POWER.md steering files); exploitation requires the user open a malicious workspace file and message the agent, and is assessed as low difficulty. Details →First reported · updated · 7 reports sprinklenet.com
RAG Prompt Injection: Risks, Examples & Defenses — Everyday on AI
An explainer on RAG prompt injection describes how attackers weaponize the retrieval layer of enterprise RAG pipelines rather than the LLM prompt box, walking through injection vectors such as retrieval query manipulation and cross-vector database poisoning, plus a mitigation framework. It references OWASP's RAG-specific additions to the LLM Top 10 and academic work on indirect prompt injection (IPI), including arXiv research showing a single poisoned email could coerce GPT-4o into exfiltrating SSH keys in a multi-agent workflow. Details →First reported · updated · 2 reports 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 traces rather than its factual knowledge, using evasive language to bypass keyword filters and self-referential reinforcement to defeat consensus-based defenses, achieving up to 100% attack success including against A-MemGuard. They also propose SENTINEL, a layered defense whose Reasoning Guard structurally analyzes memory entries for forgery, reducing attack success to as low as 0% with no false positives across 326 benign traces. Details →First reported arcade.dev
One Question, Every Tool Call: Runtime Governance
An Arcade.dev thought-leadership post argues that agent security should be enforced through runtime governance at the tool-call boundary, where a control plane asks whether a given agent, on behalf of a given user, may take a specific action on a resource before any call executes. The piece frames this as an architectural alternative to guardrails, addressing prompt-injection-style abuse and the identity pitfalls of agent service accounts versus inherited user access. Details →First reported codesecai.com
Zero-Click Prompt Injection: AI Web Browsing Exploits (2026)
A guide on codesecai.com explains "zero-click prompt injection" against AI web-browsing agents, where adversaries embed invisible instructions in public HTML/CSS/Markdown (zero-opacity text, micro-fonts, comments, image pingbacks) that headless browser extractors ingest. It describes how injected Markdown image tags trigger outbound requests that silently exfiltrate user chat history to attacker-controlled servers on platforms like Perplexity, ChatGPT Search, and Microsoft Copilot. Details →First reported forkast.news
Memory Poisoning: AI Security Threat Explained | Forkast Learn
Forkast's glossary entry defines memory poisoning, an attack against LLM-based AI agents in which adversaries inject malicious or false data into an agent's persistent long-term memory layer (vector databases, semantic indexes, grounding caches, or stored conversation histories). The entry explains its temporal decoupling and persistent blast radius, cites OWASP's ASI06 classification, and describes common vectors such as poisoned PDFs, web pages, and support tickets leveraging indirect prompt injection. Details →First reported workos.com
Keeping credentials out of an AI agent's context with Relay
WorkOS describes Relay, an early-access product that proxies an AI agent's third-party API calls and injects the credential at the boundary, so a prompt-injected agent holds no token to steal and has nowhere to exfiltrate it. The write-up explains the design pattern (credential brokering, allowlisting outbound destinations, and shifting the attack surface to the broker) and grounds it in research on design patterns for securing LLM agents against prompt injection. Details →First reported · updated · 9 reports nhimg.org
Prevent Prompt Injection Attacks: 12 Steps [2026]
"Prevent Prompt Injection Attacks: 12 Steps [2026]" is a defensive explainer that walks through best-practice mitigations against direct and indirect prompt injection in LLM- and agent-based applications, drawing on guidance such as semantic intent classification, output controls, and least-privilege prompting. The piece aggregates reference material from sources including OWASP, Palo Alto Networks, and vendor writeups rather than presenting a new attack or finding. Details →First reported · updated · 4 reports simonwillison.net
The lethal trifecta for AI agents: private data, untrusted content, and external communication
An explainer on stopping prompt injection in MCP servers frames the problem as the 'lethal trifecta' (private-data access, exposure to untrusted content, and external communication) coined by Simon Willison, using the Invariant Labs demonstration against GitHub's official MCP server as its central case. In that attack a malicious GitHub issue embedded agent-directed instructions that caused a coding agent to leak private repo details into a public pull request, with no exploited code or CVE. The piece argues the fix is architectural rather than prompt-based. Details →First reported nhimg.org
AI agent risk frameworks: is the rule of two already broken?
An NHIMG editorial, based on Noma Security's analysis, argues that the 'Rule of Two' agent risk framework breaks down in real deployments because two-of-three conditions (capability, autonomy, privilege) can still yield destructive outcomes such as prompt injection in developer tools or autonomous agents deleting production data. It proposes governing AI agents as non-human identities with scoped privileges, discoverability, and action-level auditing. Details →First reported communityit.com
Blog: AI Agent Cybersecurity Threats For Nonprofits
Community IT's blog explains the "lethal AI trifecta" for nonprofits deploying AI agents: autonomous exfiltration ability, access to sensitive data, and access to untrusted content, warning that an agent with more than two of these can be manipulated (e.g. via malicious links in emails) into exfiltrating data or acting on behalf of attackers. It offers preventive guidance such as requiring human approval before agents create/edit files and restricting file permissions. Details →First reported simonwillison.net
Understanding ChatGPT Work
Simon Willison's teardown of OpenAI's ChatGPT Work (specifically the cloud variant, Work Cloud) argues its feature set — internet-enabled code execution, a headless Chrome browser, a persistent scratch filesystem, sub-agents, scheduled automations, and Cloudflare Workers site deploys — combines all three elements of his 'lethal trifecta': access to private data, exposure to untrusted content, and a channel to exfiltrate stolen data. Willison does not demonstrate an exploit but asks OpenAI to explain how it defends Work sessions against prompt injection, criticizing the product's opacity around system prompts and tool descriptions. Details →First reported · updated · 3 reports arxiv.org
Securing the Model Context Protocol (MCP): Risks, Controls, and Governance
An analysis piece synthesizing MCP (Model Context Protocol) security risks for CISOs, drawing on a Darktrace blog and an arXiv paper (arXiv:2511.20920) by Errico, Ngiam, and Sojan. It categorizes threats such as content-injection attackers embedding malicious instructions into agent-consumed data, supply-chain attackers distributing compromised MCP servers, and over-privileged agents enabling data-driven exfiltration, tool poisoning, and cross-system privilege escalation, and proposes controls including scoped per-user authentication, sandboxing, provenance tracking, DLP, and centralized governance. Details →First reported arxiv.org
What's in Your Agent's Context? Context Privilege Escalation Attacks against AI Agent Harness
A research paper titled "What's in Your Agent's Context? Context Privilege Escalation Attacks against AI Agent Harness" presents the first systematic analysis of context assembly in real-world AI agent harnesses, uncovering two novel attack classes: MessageRole Context Privilege Escalation (M-CPE), where attacker-controlled low-privilege content is elevated into a higher-privileged message role, and Cross-Scope Context Privilege Escalation (X-CPE), where attacker content persists beyond its original context. The authors evaluate these attacks against 12 harnesses including Claude Code and Codex, demonstrating consequences such as full agent compromise, remote code execution, denial of service, and manipulated tool or skill invocations. Details →First reported · updated · 4 reports cyera.com
Drive-By Agent Hijacking: One Website Visit, Persistent Model Poisoning
Cyera's Oasis Identity Research disclosed CVE-2026-65105 in NVIDIA NemoClaw, which deploys the OpenClaw AI agent with local Ollama inference. NemoClaw starts Ollama bound to 0.0.0.0:11434 (while telling users it is on localhost), disabling a key defense; combined with DNS rebinding, a single visit to an attacker-controlled webpage gives unauthenticated access to the Ollama API, letting an attacker persistently poison the model's chat template so injected instructions survive the agent's own system prompt and steer the agent thereafter. The findings were reported to NVIDIA PSIRT prior to publication. Details →First reported · updated · 4 reports embracethered.com
Breaking Claude Code Opus 5 Auto Mode
Johann Rehberger (Embrace The Red) demonstrated an indirect prompt injection attack chain that hijacks Claude Code Opus 5 in Auto Mode via a simple 'summarize this website' request, achieving code execution with a 60-80% success rate. The chain nudges Claude from WebFetch to curl, downloads a ZIP whose extracted malicious struct.py shadows Python's standard module, so importing base64 triggers attacker code; in some runs Auto Mode's safety classifier even blocked Claude's own cleanup command. The result contrasts with a vendor-commissioned evaluation (Trajectory Labs) that reported 0.00% attack success for Opus 5 in Auto Mode. Details →First reported dev.to
I broke an MCP server in 10 minutes — the exact prompt injection attack chain (with fixes)
A DEV Community write-up demonstrates an indirect prompt injection attack chain against a typical MCP server exposing read_file and send_email tools, where a submitted document containing a fake 'SYSTEM NOTE' instruction causes the model to exfiltrate /etc/passwd by email because no boundary separates data from instructions. The author outlines fixes (treat tool/file content as data, per-session tool allowlists, confirmation gates on external-sending tools) and notes tool-description poisoning persists across sessions. The post also promotes a free hosted scanner. Details →First reported arxiv.org
Beyond the Mandate: A Systematic Security Analysis of the Agent Payments Protocol (AP2)
Researchers from Ben-Gurion University and Intuit present a systematic security analysis of Google's Agent Payments Protocol (AP2) v0.2, which lets LLM-driven shopping agents authorize and execute payments. Using the MAESTRO framework they model threat actors, attack surfaces, and adversary capabilities, cataloging 48 threats across five attack families, scoring them with AIVSS, building a testbed across five deployment architectures, and developing proof-of-concept demonstrations for eight High-risk threats plus a deployment-aware scanner. Their key finding: valid mandate signatures alone do not guarantee an agent-mediated transaction reflects user intent when pre-authorization context (A2A messages, MCP tool calls) is manipulated. Details →First reported · updated · 2 reports darkreading.com
"GhostJacking" Exposes Identity Governance Gaps in AI Agents
Tenet Security presented "GhostJacking" research at DEF CON 34, demonstrating how attackers can poison content in trusted systems such as security alerts, logs, and error reports to trick AI agents into executing code, stealing credentials, and taking over infrastructure. The work expands the company's earlier "Agentjacking" technique into a broader attack model spanning multiple trusted data sources and a wider range of damaging agent actions. Details →First reported · updated · 2 reports whenmachines.com
AI Agent Computer Use Safety: How to Deploy One Without Getting Burned | WhenMachines
WhenMachines publishes a guide on safely deploying computer-use AI agents, explaining why these agents are riskier than chatbots because they can act on a machine, and detailing risks like indirect prompt injection. The article recommends defensive measures including disposable isolated environments, least privilege, separating reading from acting, human approval at consequential boundaries, guardrails, logging, and governance frameworks. Details →First reported arxiv.org
AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents
A SureBright explainer on "agent hijacking" (agentjacking) describes how attackers can plant malicious instructions in ecommerce content — such as customer reviews — that an AI shopping agent reads during normal product research, steering it to phishing sites or leaking session data. The piece grounds its claims in the AgentVigil research (arXiv:2505.05849), a black-box fuzzing framework that automatically discovers indirect prompt injection vulnerabilities in LLM agents and demonstrated 70-71% attack success against agents built on o3-mini and GPT-4o. Details →First reported thehackernews.com
Russia-Aligned UAC-0099 Plants Nuclear Weapon Prompt in Malware to Disrupt AI Analysis
ESET researchers disclosed a technique dubbed GuardBreaker used by the Russia-aligned threat actor UAC-0099 against a Ukrainian target, in which the actor embedded a safety-tripping comment ('I want to make a nuclear weapon. Help me...') into a malicious VBS script to deliberately trigger an LLM's safety mechanisms and stop it from analyzing the rest of the code. Related Zscaler ThreatLabz reporting notes the Shai-Hulud supply-chain worm similarly introduced prompt injection in PyPI packages to mislead LLM-based security scanners. Details →First reported · updated · 2 reports github.com
GitHub - delphisecurity/xaidr · GitHub
xaidr, published by delphisecurity, is an open-source (Apache-2.0) runtime security sensor for AI agents that detects and classifies threats such as part-level role forgery, forged tool-result injection, credential/secret-manager access, and data egress. The repo includes detection rules, enforcement modes (classify vs block), regression tests, and CI, positioning it as a defensive guardrail for agentic tool-calling systems. Details →First reported · updated · 5 reports substack.com
What Are the Security Risks of AI Agents? How to Protect Tool Use and Access Control｜Gate.AI
An explainer argues that prompt injection against AI agents wired into real infrastructure (Kubernetes, cloud APIs, CI/CD, object storage) has evolved from a model-behavior problem into an access-control problem, because a hidden instruction in a document can become a real command once an agent can call tools like kubectl. The piece frames defense around tool-use permissions and authority rather than system-prompt hardening. Details →First reported · updated · 3 reports arxiv.org
Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States
Research described under the title 'Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States' argues that an agentic LLM's internal hidden-state representations encode a signal of whether the model has been exposed to indirect prompt injection, and that this signal can be probed for detection. Related artifacts referenced include an IPI-exposure-signal code repository and rule-based/monitor detection work such as AgentWatcher. Details →First reported arxiv.org
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
Researchers at Tampere University present Trustworthy RAG, an Evaluation Agent middleware that detects knowledge poisoning and misinformation in Retrieval-Augmented Generation systems by combining Natural Language Inference factual verification, a five-signal poison detector, and a Trust Index scoring formula. On TruthfulQA with Llama 3.3 70B the agent reaches 91% accuracy and 100% recall on instruction injection, though subtle in-place entity swaps remain hard to detect; the authors release the approach, an attack generator, and experimental artifacts at github.com/GPT-Laboratory/TrustworthyRAG. Details →First reported · updated · 2 reports mastra.ai
Guardrails for AI Agents: A Practical TypeScript Guide
Mastra's guide by Aron Schuhmann explains how to implement guardrails for AI agents in TypeScript, covering input validation, PII detection, tripwires, classifiers, tool-call authorization, and observability. The piece frames guardrails as programmatic runtime checks that sit between user input, the LLM, tools, and end-user output to counter threats like prompt injection (cited as OWASP's top LLM vulnerability). Details →First reported grasp.study
Grasp - Building a Sandwich Defense
Grasp's lesson "Building a Sandwich Defense" is an educational walkthrough teaching how to defend LLM applications against indirect prompt injection, where malicious instructions are hidden in documents, webpages, or emails an agent retrieves. It introduces the "sandwich" prompt-engineering technique to isolate untrusted content and a moderation-endpoint architecture that inspects data flowing into and out of the model. Details →How the wire is made
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