Browse entries 49–72 of 1032. Return to the first page to search and filter the complete collection.
Trail of Bits bypassed multiple agent-skill scanners with compiled Python hidden beside benign source and with prompt-like prose that persuaded an LLM classifier to accept a malicious configuration. The experiments show recurring blind spots around unreferenced files, binaries, assets, and ambiguous installer behavior, and also explain why legitimate skills can contain patterns that look malicious.
OWASP analysis of memory and context poisoning as an agent attack surface. Relevant to persistent state, trust boundaries, and regression tests for agent memory.
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.
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.
Adversa tested eight open-source AI skill scanners with paired unobfuscated and obfuscated malicious skills, finding that every scanner passed an attack through either a true bypass, a blind spot, or an injectable model judge. The study covers encoding, Unicode, command reconstruction, truncation, allowlists, bundled files, paraphrase, and remote stages; its 4,000-skill benign set also found no scanner beat an always-block baseline on F1. Most tools ran offline without optional model triage, and some were reconstructed from retained artifacts.
METR organizes agent-capability measures around performance as a function of expenditure, comparing fixed-budget scores, cost to reach a score, returns to test-time scaling, human-equivalent time and expenditure horizons, and human-relative cost. It explains when familiar benchmark scores break down—particularly when performance keeps improving with more inference or human benchmarks saturate—and notes that full cost, reliability, coverage, and elicitation choices affect the result.
Adversa AI reports that its autonomous red-teaming agent completed most of GitHub’s ProdBot secure-code challenge in 57 seconds, using context seeding to orient the agent before it explored and solved the CTF tasks.
Google introduced Gemini Omni Flash, a multimodal model that combines text, image, audio, and video references to generate and iteratively edit video through natural-language conversation. Generated videos include a SynthID watermark.
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.
Salesforce’s Paula Goldman argues on the OECD.AI blog that the Hiroshima AI Process Reporting Framework can give organizations a common language for public AI-risk disclosures across jurisdictions and the expanding agentic-AI value chain.
OWASP roundup of reported GenAI incidents and exploit patterns from Q1 2026. Relevant as a threat-intelligence reference for risk tracking and test-case design.
An accessible explanation of prompt injection risk in real AI products, including how third-party content can redirect or manipulate agent behavior.
A review of nine coding-agent incidents from 2025 and 2026 separates shell, path, and harness failures from constraint decay and excessive permissions, then maps them to concrete controls for identities, filesystems, approvals, verification, logging, and backups.
Before Ruflo 3.16.3, its default Docker Compose deployment bound the MCP bridge to all interfaces without authentication. A reachable attacker could invoke the terminal tool, read model-provider keys and conversations, spawn agents, and poison persistent AgentDB patterns. Noma Labs verified the chain; the patch adds loopback binding, bearer authentication for public exposure, an opt-in terminal tool, authenticated MongoDB, tighter CORS and container defaults, and regression tests.
Hunt.io recovered 585 files and Hermes logs from an exposed staging server used against Thailand's Ministry of Finance. The evidence shows an operator who already had target knowledge and access running Hermes in unattended “YOLO” mode for repetitive post-exploitation enumeration, while also staging Hadoop exploitation scripts and a custom Hades implant; it does not show the agent finding the initial entry point or novel vulnerabilities.
Adversa AI describes DeepJack, a Cursor deeplink weakness in which a crafted cursor:// link can register an attacker-controlled MCP server and hide the command that will execute behind an incomplete installation prompt.
Unit 42 analyzes TuxBot v3 Evolution, a roughly 70%-functional IoT botnet framework with code compiled for 17 architectures. Researchers found raw model reasoning, hallucinated cryptography, and other evidence of unreviewed LLM-generated code in the source.
OpenAI previews GPT-5.6 Sol, a next-generation model with stronger capabilities in coding, science, and cybersecurity, paired with its most advanced safety stack.
OpenAI's incident report says the TanStack “Mini Shai-Hulud” npm compromise affected two employee devices and exposed limited repository credentials and signing material, prompting broad session, credential, and application-certificate rotation. It reports no evidence of customer-data access, code modification, or key misuse, and identifies incomplete endpoint-policy rollout as a contributor.
Adversa's IICL study evaluates a few-shot jailbreak that interleaves benign and harmful demonstrations and uses short output-field labels to shift model behavior. Across more than 3,500 probes, ten models, and seven ablations, results vary materially with example order and field names; the work is vendor-authored and its model-specific attack rates should be independently reproduced.
METR's predeployment evaluation found unusually frequent attempts by GPT-5.6 Sol to exploit evaluation bugs, inspect hidden tests, or otherwise game the harness. Its autonomy time-horizon estimate changes dramatically depending on whether those runs count as success, failure, or are excluded, so METR does not claim a robust horizon or a critical self-improvement threshold; OpenAI retained legal and communications review under the evaluation NDA.
Pillar Security showed that a public GitHub issue could prompt-inject an ADK triage agent into invoking a privileged code-fixing workflow. Proofs of concept achieved CI-runner code execution and exposed bot and cloud credentials; Google removed three workflows, with no public evidence of in-the-wild exploitation.