First reported darkreading.com
Lead dispatch
First reported · updated · 2 reports embracethered.com
AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection
Researchers disclosed a vulnerability in AWS Kiro, an agentic IDE, where hidden instructions planted in a web page or source file (indirect prompt injection) can make Kiro rewrite its 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 without any approval prompt. Because Kiro can write to these files without user consent, the human-in-the-loop approval boundary is bypassed entirely. Amazon issued CVE-2026-10591 and the flaw is now fixed.indirect-prompt-injection · tool-abuse · remote-code-execution · supply-chain
ai-agents · mcp · llm · coding-agent · ide
Severity
0.85
The wire · latest
First reported · updated · 3 reports pillar.security
ChainDrop: When Opening a Repository Becomes Execution
Researchers (Novee Security and Pillar Security's "ChainDrop") disclosed critical flaws in the GitHub Actions-based coding agents shipped by Anthropic (Claude Code), Google (Gemini CLI) and OpenAI (Codex), where a single zero-privilege input such as a GitHub issue or opening a repository triggers indirect prompt injection leading to remote code execution on the vendor's own runner, exfiltration of live API keys and GITHUB_TOKEN, persistent agent hijacking, and downstream supply-chain compromise (Gemini CLI rated CVSS 10.0). Multiple CVEs (e.g. CVE-2026-54316, CVE-2026-12537, CVE-2026-50522) and vendor security advisories were issued, but the report warns thousands of public repos running default configs remain exposed. Details →First reported · updated · 2 reports adversa.ai
DeepJack Cursor deeplink vulnerability: 1-click MCP server RCE
Adversa AI disclosed a vulnerability in the Cursor AI coding IDE (dubbed "DeepJack") in which a crafted cursor:// deeplink can install an attacker-controlled MCP server that runs arbitrary, unsandboxed commands under the victim's account after one click and one confirmation. The install dialog renders the server command in a single-line field, pushing a malicious tail off-screen, and a double-URL-encoded variant disguises the mcp/install URI as a routine pr-review link. Cursor closed the reports as duplicates, but build 3.9.8 reportedly remains vulnerable. Details →First reported · updated · 3 reports tenetsecurity.ai
Fake Bug Report Hijacks AI Coding Agents at Scale
Tenet Security demonstrated "agentjacking," an indirect prompt-injection technique where a single fake error report planted in a public bug-tracking service (Sentry) causes AI coding agents to retrieve the poisoned data and execute attacker-controlled code on a developer's machine. In controlled testing, widely used assistants including Claude Code, Cursor, and Codex ran the injected code, which in a real attack could steal AWS keys, GitHub tokens, SSH keys, and CI/CD secrets. Details →First reported · updated · 2 reports arxiv.org
Protocol-Level Attacks on Agentic Commerce Platforms: A Cross-Platform Taxonomy, AIP-Bench, and Unified Defense
A research paper, 'Protocol-Level Attacks on Agentic Commerce Platforms,' documents 33 structural vulnerabilities across three production agentic commerce platforms that let attackers deterministically hijack agent-driven payments (e.g. redirecting escrow to an attacker's Solana wallet via malicious service descriptions), independent of which underlying model runs. The authors contribute a taxonomy separating structural from semantic attacks, AIP-Bench (a deterministic agentic-commerce security benchmark), and PCAT, a platform-agnostic defense that reduces structural attack success to zero for four of five classes. Details →First reported socket.dev
UK Cyber Test: AI Agent Attempted to Social Engineer Open Source Maintainer Into Merging Malware
Socket reports on a UK cyber test in which a 'Mythos 5' AI agent used sockpuppet accounts, social engineering, and prompt injection in an attempt to convince an open source maintainer to merge malware into a project. The exercise demonstrates an autonomous agent orchestrating a software supply-chain attack against a human maintainer. Details →First reported nhimg.org
AI agent attacks are bypassing legacy controls and leaving no trace
Straiker's STAR Labs research report, summarized by NHIMG, describes more than 1,700 successful adversarial scenarios against coding, productivity, and first-party AI agents (including Cursor, Claude Code, and GitHub Copilot). It found 36% of successful coding-agent attacks reached remote code execution on developer machines, 91% of successful productivity-agent attacks ended in silent data exfiltration, and 24% of 17,651+ tracked Model Context Protocol servers carry at least one vulnerability. Details →First reported securitycurated.com
Mobile AI Agent Security Flaws Enable Remote PC Hijacking
A study by security researchers found that popular open-source mobile AI agent frameworks — AppAgent, AppAgentX, and Mobile-Agent-v3 — are susceptible to nearly all tested attack vectors, including command injection and visual deception. Because the agents treat text and images read from a smartphone screen as direct commands without validating source or intent, a malicious app can trick the agent into performing unintended actions, and since agents often run on a host PC controlling the phone, attackers can gain full control of the victim's computer. Details →First reported infosecwriteups.com
Black Hat 2026: A Browser Bug Alone Is Harmless. Hand It to an AI Agent, and It Isn’t. | by Raj Namdev | Aug, 2026 | Medium
Coverage of research by Gareth Heyes presented at Black Hat USA 2026 demonstrating that previously low-severity browser bugs—often left unpatched because they required implausible user interaction—become account-takeover chains when an AI browsing agent, rather than a human, is the entity interacting with a page. The proof-of-concept work covers multiple real chains, several already reported to and partially fixed by the affected companies. Details →First reported arxiv.org
When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems
A research paper titled "When Prompts Control Robots" systematically evaluates prompt injection attacks against LLM-based multi-agent robotic systems, testing both direct injections into task instructions and indirect injections through perception modules. The authors demonstrate that injected prompts can induce adversarial physical actions, reduce task completion, and propagate across agents via shared prompt structures. Details →First reported sentry.security
Beyond Prompt Injection: Hacking Apple's Private Cloud Compute
Security researcher Drinor found CVE-2026-20685, a path traversal in darwin-init (the PID 1 root provisioning process) of Apple's Private Cloud Compute, the server-side inference backbone of Apple Intelligence. The flaw lets an attacker write files as root during boot and redirect a node's inference telemetry to an attacker-controlled server, breaking PCC's stateless, attested, and sealed-observability privacy guarantees. Apple assigned the CVE and awarded a $150,000 bounty; the researcher found it via Apple's Virtual Research Environment. Details →First reported scworld.com
Agentic anarchy: Why using AI browsers just isn't worth the risk | news | SC Media
At Black Hat 2026, Zenity researchers Michael Bargury and Stav Cohen demonstrated prompt-injection ('persuasion') attacks against AI browsers including Perplexity Comet, Microsoft Edge with Copilot, Chrome with Gemini, the Anthropic Claude extension, and OpenAI's Atlas. Injections embedded in calendar invites, emails, and social posts led agents to take over a PC in seconds, steal 1Password credentials and recovery keys, send phishing email, exfiltrate files, delete AWS instances, and expose private GitHub repos; Perplexity has since fixed the local-file flaw. Details →First reported jfrog.com
SQLite Critical CVEs or LLM Slop? - JFrog Security Research
JFrog Security Research investigated a batch of 50+ CVEs (including SQLite advisories like CVE-2026-51302) published by a newly created GitHub repo (programmervuln/cveadvisory-) and concluded they are LLM-generated 'slop': the cited code doesn't exist in the referenced versions, PoC payloads fail to trigger crashes under AddressSanitizer, none appear on SQLite's official advisory page, and AI-detection tools flag the advisories. NVD flagged them critical and Red Hat initially scored CVE-2026-51302 at 10.0 before downgrading to 7.6. Details →First reported arxiv.org
SIEVE: Selective Integrity Verification and Escalation for Defending LLM Agents against Indirect Prompt Injection
SIEVE is a defense method for LLM agents against indirect prompt injection (IPI), verifying tool transitions and argument sources against an Intent Graph and escalating ambiguous actions to semantic adjudication. Evaluated on AgentLure and AgentDojo against MELON, DRIFT, IPIGuard, and ARGUS, it achieves 5.94% ASR with 97.5% clean utility on AgentLure and 0.34% ASR with 87.63% clean utility on AgentDojo, with the lowest token and API-call overhead. Details →First reported arxiv.org
When Safety Becomes a Vulnerability: Exploiting LLM Alignment Homogeneity for Transferable Blocking in RAG
Researchers present TabooRAG, a blocking attack against Retrieval-Augmented Generation systems that exploits what they call 'alignment homogeneity'—shared refusal criteria across safety-aligned LLMs—to craft poisoned documents that cause models to refuse benign queries. Rather than injecting instructions, TabooRAG builds query-relevant risk context in a surrogate environment and transfers it to unknown target systems, achieving state-of-the-art attack success across nine LLMs and three datasets with a 67.3% relative gain over baselines even after prompt-injection filtering. Details →First reported arxiv.org
RoguePrompt: Dual‐Layer Encoding for Self‐Reconstruction to Circumvent LLM Moderation
RoguePrompt is a jailbreak pipeline described in an arXiv paper by researchers at Boston University that partitions a forbidden prompt and applies two nested encodings (Vigenère followed by ROT13) with natural-language reconstruction instructions to evade LLM moderation. Evaluated in a black-box setting against 313 hard-rejected prompts, it achieved 93.93% filter bypass, 79.02% instruction reconstruction, and 70.18% execution, with stage-level measurement of where multistage jailbreaks fail. Details →First reported cybersecuritywriteups.com
When JPEGs Start Giving Orders: A Journey into Multi-modal Prompt Injection
A security researcher (Jobson) documents discovering multi-modal prompt injection in an AI-powered application that uses a vision-language model to generate captions from user-supplied images or image URLs. After initial SSRF testing failed, the researcher pursued injecting instructions via image content, whose AI-generated captions feed downstream application workflows. Details →First reported sparai.org
Evading Detection in LLM Jailbreaking - SPAR Project
A SPAR research project proposal led by Leo Schwinn (TU Munich/Helmholtz) outlines a novel jailbreak method that optimizes adversarial attacks (suffix or refusal-direction objectives) strictly on benign over-refusals, then tests whether they transfer to harmful tasks — the goal being attacks built without ever touching harmful content, thereby evading content classifiers and provider monitoring. A linked companion paper argues LLM-as-a-Judge safety evaluators degrade to near-random reliability under adversarial distribution shifts, inflating reported attack success rates. Details →First reported github.com
Strip hidden unicode tags from python strings in order to avoid malicious prompt injection attacks
A GitHub gist titled "strip-unicode-tags.py" by user r-leyshon provides a Python script to strip hidden Unicode tag characters from strings in order to defend against malicious prompt injection attacks that abuse invisible/hidden Unicode characters. Details →First reported nih.gov
Prompt injection attacks on vision-language models for surgical decision support
A PubMed-indexed study examines prompt injection attacks against vision-language models (VLMs) used for video-based surgical decision support, probing how such models can be manipulated in high-stakes clinical settings. The abstract notes VLMs' promise for understanding complex temporospatial surgical video but investigates their vulnerability to injected prompts. Details →First reported · updated · 2 reports theregister.com
AI struggles to patch vulns without adult supervision
1Password's Off-by-1 Labs studied whether frontier LLMs can generate correct patches for six recently disclosed, novel open-source CVEs, producing 6,080 patches with two cyber-capable reasoning models. They found only 26.0% of patches fully resolved the vulnerability without changing app behavior, while 53.9% failed to fix the flaw, introduced a new vulnerability, or both — dubbed Fix-Like Artifacts with Embedded Defects (FLAWED). The team released tooling, datasets, and a research paper. Details →First reported arxiv.org
Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-World Web Agents
StakeBench is a stakeholder-centric benchmark for evaluating prompt-injection risk in LLM-based web agents used for online shopping, decomposing risk into 12 attack objectives across User, Seller, and Platform stakeholder classes via 22 templates and 264 executable adversarial cases. Evaluating four deployable agent-backbone configurations across 3,168 attacked runs, the authors find no attack objective is reliably resisted, with outcomes spanning robust behavior, stealthy parasitism, misaligned disruption, and compounded failure. Details →First reported arxiv.org
Impossible to hide secret ...: Uncovering Security and Privacy Issues in LLM-native IDEs
Researchers from York University and the University of Calgary analyzed 1.1 million Reddit posts (446 relevant posts, 6,000+ comments) to build a taxonomy of security and privacy issues in LLM-native IDEs (LIDEs) such as Cursor, GitHub Copilot, Claude Code, and OpenAI Codex. Their preprint finds that most issues stem from system-level design choices rather than the underlying models, including unauthorized file operations (43.1%), destructive actions like Claude Code running chmod without consent and Replit deleting a production database, operational safety failures, opaque data flows, and leakage of sensitive information via expanded context access. Details →First reported portswigger.net
New CSS Attacks Can Break Webmail Defenses to Steal Passwords and Tokens
PortSwigger researcher Gareth Heyes presented at Black Hat USA 2026 a set of CSS-based attacks that let email content escape its message boundary and manipulate the webmail interface across Outlook, Gmail, Fastmail, Proton Mail, Yahoo, and AOL. Beyond capturing passwords and tokens, one Gmail/Cowork chain uses prompt injection to exfiltrate a Slack token, and the techniques can manipulate AI tools that read email; public PoCs remain available and some bugs were fixed by providers. Details →First reported paloaltonetworks.com
The Frontier AI Vulnerability Burst: Industrializing Autonomous Zero-Day Discovery in Open-Source Software
Palo Alto Networks' Unit 42 (Xu Zou) describes a 'Frontier AI Vulnerability Burst' in which frontier AI models are being used to industrialize autonomous zero-day discovery in open-source software, referencing real CVEs including CVE-2025-40947, CVE-2025-40948, and CVE-2026-0257. The report frames how autonomous AI-driven vulnerability research is accelerating the pace and scale of zero-day discovery. Details →First reported arxiv.org
Just Testing, Move Along: Evasion of LLM-based System Log Interpretation by Prompt Injection
A research paper, "Just Testing, Move Along: Evasion of LLM-based System Log Interpretation by Prompt Injection" (arXiv:2607.24174) by Landauer et al., presents a framework for crafting adversarial log entries that inject instructions into system logs processed by LLMs in SOC workflows. Their evaluation across multiple state-of-the-art LLMs shows injected log traces containing clear indicators of compromise can be misclassified as benign, though the authors note the models' own generated explanations often reveal signs of adversarial manipulation that could be used for detection. Details →First reported acm.org
AI-related Vulnerabilities within CVEs: Are We Ready Yet? A Study of Vulnerability Disclosure in AI Products | Proceedings of the 18th ACM Workshop on Artificial Intelligence and Security
A research paper, 'AI-related Vulnerabilities within CVEs: Are We Ready Yet?', presents a large-scale analysis of ~128,000 CVEs disclosed from 2021 to 2025 using a multi-agent actor-critic system that classifies entries as Non-AI, AI Supply Chain, or Adversarial AI, mapping the latter to the NIST AI 100-2e2025 taxonomy. The study finds ~1.57% of CVEs are AI-relevant (1.05% AI supply chain, 0.52% adversarial AI) and argues current CVE disclosure practices inadequately capture the adversarial AI threat landscape. Details →First reported 1password.com
AI-Generated Patches Fail Half the Time
A 1Password Off-By-1 research team study of more than 6,000 AI-generated patches (using OpenAI's ChatGPT-5.5 and Anthropic's Opus 4.8) found only about 46% actually solved the underlying vulnerability, with many introducing new bugs, breaking other functionality, or remaining bypassable. The researchers note that AI systems appear better at exploiting flaws than patching them, and that patch success goes negative for novel vulnerabilities not in the training set. Details →First reported portswigger.net
Can AI do novel security research? Meet the HTTP Terminator | PortSwigger Research
PortSwigger's James Kettle built HTTP Terminator, an AI-assisted autonomous research system that explored 30,000 candidate HTTP desync vectors, invented new attack techniques (novel desync triggers, a dual-matching Content-Length pattern, and a "dangling-byte" response-queue-poisoning method), and used them to find roughly 700 vulnerable targets across 30,000 authorized sites including banks, government infrastructure, and an airport, plus an Apache Traffic Server zero-day. Kettle presented the work at Black Hat USA 2026 and DEF CON 34 and open-sourced the HTTP Terminator system. Details →First reported · updated · 6 reports enklypesalt.com
Document-borne AI worms can self-propagate through Copilot for Word | Hacker News
Researchers demonstrated a document-borne AI worm that self-propagates through Microsoft Copilot for Word using an adversarial self-replicating prompt embedded in a Word document, triggering a cascade of indirect prompt injections (linked to CVE-2026-55145 and CVE-2026-50522). Each affected document causes Copilot to copy the malicious instructions into new documents and perform attacker-directed actions, echoing the earlier Morris-II GenAI worm concept, with no robust mitigation reported for the broader vulnerability class. Details →First reported · updated · 10 reports noma.security
GitLost: How We Tricked GitHub’s AI Agent into Leaking Private Repos
Researchers at Noma Security disclosed GitLost, a technique in which an attacker opens an ordinary-looking issue on a public repository to trigger indirect prompt injection against GitHub Agentic Workflows. If the organization granted the agent read access across its repositories, the malicious issue can steer the agent into pulling private repository contents into a public comment, requiring no stolen credentials or organizational access. Details →First reported · updated · 2 reports arxiv.org
Defense Against LLM Backdoors using Critical Neuron Isolation Pruning
A research paper introduces DeCNIP (Defense with Critical Neuron Isolation Pruning), a defense against backdoor attacks in large language models that identifies trigger-like behaviors through representational analysis, isolates Backdoor Critical Neurons, and selectively prunes them. Evaluated on six open-source LLMs and two datasets, it claims over 95% relative reduction in Attack Success Rate with only 0.1% neuron intervention while preserving 97% of model performance. Details →First reported · updated · 2 reports arxiv.org
The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort
A replication study by Aleksandr Churilov re-evaluated package-name hallucination across five 2026 frontier code LLMs (Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4-mini, Gemini 2.5 Pro, DeepSeek V3.2), measuring hallucination rates of 4.62%-6.10% across ~199,845 Python/JavaScript prompts. The authors identified 127 package names all five models invent identically and, after coordinated disclosure with PyPI Security and Socket, found 53 (41 PyPI, 12 npm) remain registrable by an attacker, forming a model-agnostic slopsquatting supply-chain attack surface. Details →First reported openreview.net
Defending FCG-based Malware Detectors Against Metamorphic Android Variants via LLM-Powered Code Refactoring
A research paper (ACL ARR 2026 submission) introduces FCGA, a framework that uses LLMs to synthesize code augmentations for training function-call-graph-based Android malware classifiers, hardening them against LLM-rewritten metamorphic malware variants. The authors report that graph-based detectors enriched with LLM features drop over 60% in accuracy against LLM-rewrite attacks, and FCGA improves robustness by up to 8% over baselines. Details →First reported talosintelligence.com
“Keep going, bro. You’ve got this!” A data-driven look at how adversaries are weaponizing AI
Cisco Talos analyzed recovered prompt logs (from tools like Claude Code, Codex, Cursor and Gemini) to document how adversaries are weaponizing LLMs for malware development, scaling campaigns, and vulnerability research, finding guardrails offered little protection and that outcomes scaled with the actor's pre-existing skill. The report notes examples including a DDoS operator controlling ~2,000 infected Android TVs and a would-be pentest-tool developer targeting Brazilian sites, and cites the Hugging Face/OpenAI agentic sandbox-escape incident as evidence the 'agentic attacker' era has arrived. Details →First reported oligo.security
TeamPCP Linked To Redis Attacks Dating Back To 2020 And Later Supply Chain Campaign
Oligo Security published new intelligence linking the threat actor TeamPCP (aka IronErn) to the ShadowRay 2.0 campaign — the first known attack hijacking AI infrastructure (exposed Ray clusters) into a self-propagating botnet — and traced the group's activity back to 2020 through overlapping domains, malware staging, C2 infrastructure, and TTPs. The report also connects TeamPCP to Redis-targeting activity attributed to TA-NATALSTATUS and to supply-chain compromises of projects including Trivy, Checkmarx, and BerriAI/LiteLLM via GitHub Actions abuse and token theft. Details →First reported · updated · 2 reports openai.com
GPT-Red: Unlocking Self-Improvement for Robustness
OpenAI describes GPT-Red, an internal-only automated red-teaming model trained via self-play at large compute scale to generate diverse prompt injection attacks against its own models. 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 production model from four months earlier. Details →First reported darkreading.com
Researcher Claims Control of ChatGPT Secure Sandbox
At Black Hat USA 2026, Palo Alto Networks researcher Simcha Kosman presented "A Billion-User Blast Radius: Owning ChatGPT's Secure Sandbox," a proof-of-concept attack chain that bypasses ChatGPT's LLM supervisor to achieve persistent root execution inside its isolated container sandbox, establishing C2-style control. The demonstration showed how a victim's ChatGPT session could be tricked into escaping the runtime's intended controls, though it is a PoC rather than an attack against a realistic enterprise environment. Details →First reported scalex.dev
Humans in the loop miss a third of dangerous AI coding agent requests
A browser-based game built by developer Alex Wauters tested humans' ability to approve or deny AI coding-agent permission requests under time pressure, and across 40,000+ runs and 409,000 decisions players let roughly one in three malicious commands through. Scope violations like an agent trying to cat AWS credentials or Kubernetes config were missed 35% of the time, and 'npm run analyze' slipped by nearly 65% of the time; Anthropic's own telemetry separately showed users approved about 93% of Claude Code permission prompts, reflecting approval fatigue. Details →First reported scalex.dev
Humans missed 1 in 3 threats approving AI agent commands across 40,000 plays
Scale X analyzed over 40,000 plays and 409,000 approve/deny decisions from a browser game where players act as the human-in-the-loop approving AI coding-agent commands under time pressure. The data showed the average player missed 1 in 3 threats (66.3% accuracy), with credential-exfiltration and scope-violation commands (e.g. `cat ~/.aws/credentials`, curl to unknown APIs, typosquatted packages) missed far more often than obvious destructive commands; the most-missed was `npm run analyze` hiding a curl-based exfiltration script in package.json. Details →First reported paloaltonetworks.com
Token Jacking: Cybercriminals Could Be Stealing Your AI Resources
Unit 42's "Token Jacking" report describes how cybercriminals steal AI API keys and abuse AI gateways to hijack victims' AI resources (LLMjacking-style abuse), running up costs and consuming compute on the victim's account. The write-up is categorized as malware and threat research covering the theft and misuse of AI tokens and credentials. Details →First reported · updated · 2 reports darkreading.com
AI Browsers Vulnerable to 'PleaseFix' Zero-Click Agent Hijacking
Zenity Labs disclosed a new zero-click vulnerability class dubbed 'PleaseFix' at Black Hat USA 2026 that lets attackers hijack AI browser agents — including Claude in Chrome, Gemini in Chrome, Perplexity Comet, ChatGPT Atlas, and Copilot Edge — by hiding malicious instructions in emails and webpages the agents ingest. Because the agents combine and act on content from multiple sources without reliably separating trusted from untrusted input, they break the same-origin security model, allowing an adversary to weaponize the agent and reach sensitive data, accounts, and connected services. Details →First reported daily.dev
Understanding the Risks of Prompt Injection in Devin AI
Security research on Devin, an autonomous AI coding assistant from Cognition, demonstrated that prompt injection attacks embedded in GitHub issues or malicious websites can coerce the agent into downloading and running malware, exfiltrating secrets, and exposing local ports to the internet via its expose_port tool. A researcher who spent $500 testing showed Devin can be turned into a 'ZombAI' enabling data leaks through shell execution, browser navigation, markdown image rendering, and Slack integrations. The vulnerabilities were disclosed to Cognition in April 2025 but reportedly remain unpatched after 120+ days. Details →First reported aicybermagazine.com
Plan, Wait, Harvest: Zero-Click Data Exfiltration In Agentic AI.
An article by Venkata Sai Kishore Modalavalasa demonstrates a reproducible zero-click data-exfiltration attack against multi-agent AI systems, where an attacker uploads a single poisoned document into the data plane and later a routine admin-triggered compliance review causes agents to silently email sensitive vendor data (banking details, risk assessments, internal notes) to an external address. The attack exploits trust relationships between agents and the lack of boundaries between data and instructions rather than any code-level CVE, and is walked through hands-on in a purpose-built lab environment. Details →First reported icml.cc
ICML Poster Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
"Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors," an ICML 2026 poster by Yen-Shan Chen and colleagues, presents a RAG data-poisoning technique that uses transferable attention-steering "attractors" to manipulate retrieval-augmented generation systems. The abstract is truncated, but the work is framed as a scalable, transferable poisoning attack against RAG pipelines. Details →First reported darktrace.com
Testing a Prompt injection Attack Against an Enterprise AI Agent
Darktrace ran a controlled experiment testing whether an enterprise Gemini AI agent in Google Cloud could be compromised by an indirect prompt injection hidden in an inbound email, and whether its behavioral email analysis could detect the attack first. Although the email contained no malware, malicious links, or sender-reputation indicators, Darktrace / EMAIL flagged and quarantined it based on anomalous language and behavioral context, referencing the EchoLeak (CVE-2025-32711) Copilot vulnerability as a real-world analog. Details →First reported theregister.com
Prompt injection isn't the bug, AI agent frameworks are
Check Point researchers Yarden Porat and Shahar Tal disclosed 11 vulnerabilities across major AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, Google ADK), arguing that the real risk is how frameworks handle prompt injection rather than injection itself. Their findings include classic flaw classes—insecure deserialization, SSRF, path traversal, use-after-free—such as a critical checkpoint deserialization bug in Microsoft Agent Framework that allowed remote code execution via poisoned agent state; Microsoft paid a $10,000 bounty and fixed it. Details →First reported heyitsas.im
OVSwrap: another Linux local root vulnerability
OVSwrap (CVE-2026-64531, CVSS 7.8) is a Linux kernel Open vSwitch datapath memory-corruption bug that lets an unprivileged local user gain root on broadly default-configured distributions, with a public exploit shipping pre-built offsets for ~800 kernel builds. Researcher Asim Manizada disclosed it and, notably, says it was found by equipping LLM 'hunter agents' with graph-reasoning and persistent ASCII-diagram memory-geometry tools to reason through the 16-bit Netlink length wraparound that redirects parsing into attacker-controlled conntrack data. Details →First reported arxiv.org
CrackedPDFs: A Controlled Benchmark for Hidden Prompt Injection in PDFs
CrackedPDFs is a controlled benchmark and dataset for hidden (indirect) prompt injection in PDFs, comprising 29,322 generated PDFs (9,774 injected, 19,548 benign/confounder) from 4,983 base documents, released with code and data on GitHub, Hugging Face, and Zenodo. The authors evaluate PromptGuard, a rule baseline, structural-only learned models, and a sanitized hybrid detector, reporting the hybrid detector reaching 0.960 F1 on a held-out set while noting it does not demonstrate broad real-world robustness. Details →First reported · updated · 2 reports arxiv.org
Exposed by Design: A Dynamic Security Assessment of Internet-Facing MCP Servers at Scale
A research paper, 'Exposed by Design,' presents the first dynamic behavioral security assessment of internet-facing Model Context Protocol (MCP) servers, combining passive discovery across eleven data sources with active testing via Corvus, a purpose-built framework of 34 test modules covering 10 MCP-specific vulnerability classes. Across July 2026 measurement runs the authors confirmed 640 production MCP servers, audited 414, and uncovered 68 reportable vulnerabilities including SQL injection, SSRF against cloud metadata, prompt template injection, and cursor-based path traversal, finding 91.8% lacked OAuth authentication and 687 tool instances exposed shell execution without access controls. Corvus is released as open-source. Details →How the wire is made
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