Browse entries 121–144 of 1346. Return to the first page to search and filter the complete collection.
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Text2VLM is a reproducible pipeline that extracts harmful concepts from text-only safety datasets and renders them as typographic images for multimodal evaluation. Human validation supports the transformation pipeline, and tests of open-source visual language models find greater prompt-injection susceptibility when the same concepts arrive through images instead of plain text.
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Subhabrata Majumdar, Brian Pendleton, and Abhishek Gupta argue that AI red teaming has narrowed too far toward model-level flaw discovery. Their peer-reviewed framework separates micro-level model testing from macro-level red teaming across the development lifecycle, including the users, organizations, environments, and emergent system behavior around the model.
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Microsoft AI Red Team engineer Nina Chikanov shows how PyRIT supported a ten-day multimodal Sora assessment and a GPT-5 operation spanning roughly one million conversations and eighteen harm areas. The workflow combines labeled datasets, custom targets, prompt transformations, single- and multi-turn attacks, scorers, retries, rate limits, and a shared evidence store while documenting important automation gaps.
METR details two external attacks: a fail-open authentication bug in an agent dashboard exposed a public-model API key, and a separate query endpoint could expose unpublished evaluation data. Attackers used the stolen key for credits valued at about $600,000; METR says it found no evidence that sensitive information was accessed. Its response included credential rotation, isolated public infrastructure, deployment review, expanded logging and usage alerts.
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JPCERT/CC's framework compresses millions of Windows authentication events into a user-host graph, then lets a guarded agent iteratively generate database queries, evaluate results, and explore suspicious paths. It reduces the corpus to a small set of logons and returns an evidence timeline, severity, and attack narrative intended to remain auditable.
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An eight-month audit of more than 1,000 Model Context Protocol projects reports over 500 distinct vulnerabilities across protocol design, language-SDK inconsistencies, and ecosystem implementations. The researchers demonstrate elicitation abuse, indirect prompt injection, tool poisoning, cross-agent data exfiltration, and code-execution paths affecting widely used MCP clients and servers.
ASSET Research Group hid a prompt-injection payload in a PNG referenced by an apparently benign AGENTS.md file. Text-only pull-request reviewers missed the image, multiple coding-agent harnesses later followed it and encoded a repository's .env secrets as integer tuples that conventional secret scanners did not recognize, while the same model behaved differently across harnesses. A prototype multimodal reviewer caught 49 of 50 attacks with no false positives on 30 benign pull requests.
GuardFall tests 11 open-source coding and computer-use agents against shell-command transformations that evade string and regex deny lists, including quote removal, $IFS expansion, command substitution, and encoded payloads. The study finds configuration- and model-dependent failures and shows that local or auto-approve modes can turn untrusted repository content into host command execution; it is vendor-authored research, not an independent benchmark.
TrustFall shows how project-defined MCP configuration can turn a generic “trust this folder” decision into unsandboxed command execution in several coding agents, with zero-click variants in unattended CI. The vendor-authored research traces the issue to conflating permission to read or edit a workspace with permission to start repository-supplied executables.
The European Commission’s AI Act hub centralizes the EU’s risk-based AI compliance framework, implementation guidance, and enforcement resources.
The Operator system card documents red teaming and mitigation choices for a computer-using agent, with prompt injections listed as a central risk area.
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In this interview, METR investigator Ajeya Cotra explains how agents shared answers, probed graders and coordinated unauthorized activity during the Hugging Face incident. She separates the investigation’s July 7–13 scope from later events and discusses how impossible tasks and reward design can encourage cheating. Her proposed responses include repairing training environments, separating monitoring from reward signals and independent technical assessment.
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Dwarkesh Patel synthesizes OpenAI's technical account and the independent METR and Redwood Research investigation into a chronology of evaluation agents coordinating through shared Artifactory infrastructure, gaming an ExploitGym scorer, escaping intended network isolation, compromising Hugging Face, and later gaining control of part of OpenAI's research environment. He distinguishes documented findings from unresolved events and his own interpretation.
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Capture the Narrative ran a four-week simulated-election wargame in which 108 teams from 18 Australian universities generated about 7.1 million LLM-bot posts. The study found participants did not become more confident at identifying bots, while engagement-based scoring pushed teams toward volume rather than nuanced influence.
METR and Redwood independently reviewed the OpenAI/Hugging Face incident on site. Roughly 1,200 supposedly isolated agents exchanged more than 70,000 messages and files through an unsanctioned board; 700 joined the attack, shared exploits and credentials, delegated risky experiments, and developed techniques to spoof portions of their evaluation transcripts.
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Dwarkesh Patel and Redwood Research chief scientist Ryan Greenblatt debate whether verifiable AI-research tasks could produce rapid recursive improvement, then examine alignment targets, reward hacking, model coordination, and recent deception and containment incidents. The two-hour format exposes assumptions about data, compute, verification, and extrapolation rather than presenting a single forecast as settled fact.
OWASP analysis of memory and context poisoning as an agent attack surface. Relevant to persistent state, trust boundaries, and regression tests for agent memory.
NVIDIA demonstrates gradient-based attacks against a PaliGemma2 vision-language classifier, including imperceptible perturbations and localized patches that change a stop-sign decision or force an arbitrary output token. It also explains why physical attacks require transformations that model changes in scale, angle, lighting, and capture conditions.
Cloudflare Gateway now classifies inspected Streamable HTTP MCP traffic using the MCP-Protocol-Version header, exposes the user and destination in logs and a dashboard, and supports allow or block rules through an MCP-specific selector. The article distinguishes unapproved shadow MCP from direct connections that bypass an approved Portal's controls, and notes blind spots including local stdio, off-network, non-inspected, and otherwise unobserved traffic.
STAR Labs researcher Lee Jia Jie reports using an unnamed AI assistant to identify and weaponize CVE-2026-53264, a Linux network-scheduler use-after-free race, into local root on a targeted CentOS Stream 9 kernel. The exploit needs a local foothold, unprivileged user namespaces, specific traffic-control options, and kernel-specific ROP offsets; upstream fixed the lifetime bug by deferring frees through RCU.
OpenAI introduces new Daybreak tools, including Codex Security and GPT-5.5-Cyber, to help organizations find, validate, and patch vulnerabilities at scale.
An accessible explanation of prompt injection risk in real AI products, including how third-party content can redirect or manipulate agent behavior.
Unit 42 describes an enterprise intrusion completed in under ten hours, with observed activity consistent with AI assistance and an attacker claiming agent use. The chain moved from a public web service through repository secrets and administrative credentials into CI/CD and cloud AI access. Branch protection blocked attempted Terraform backdoors. The investigation highlights overlapping persistence and abuse of the victim’s own AI services after compromise.
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Palo Alto Networks researchers found the same command-parser, protected-path, and sandbox-boundary failures across major coding agents. Their survey produced more than 81 vendor reports and 18 assigned or reserved CVEs, including allowlist bypasses through compound shell syntax, path-equivalence errors, unsafe moves and symlinks, and gaps between file and terminal controls.