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LLM-HALLUCINATION

Llm-Hallucination

4 reports
All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Jailbreaks Adversarial ML Model Theft Data Poisoning Deep Signal Intelligence Report AI Governance AI Security AI Security Tools Identity & Access Management Security Operations
AI Coding Agents Exploited via Hallucinated Package Names

AI Coding Agents Exploited via Hallucinated Package Names

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 BleepingComputer

Researchers from Tel Aviv University, Technion, and Intuit have demonstrated that AI coding agents across tools like Cursor, Copilot, and Gemini CLI predictably hallucinate package, domain, and repository names that attackers can pre-register to deliver malicious code. The attack—variously branded slopsquatting, phantom squatting, and HalluSquatting—requires no phishing, no stolen credentials, and no direct user interaction, only an automated agent with permission to fetch external resources. Because agents handle delivery autonomously and hallucinations are reproducible at up to 100% consistency, the technique scales to botnet-level compromise without traditional malware infrastructure.

Browser Ransomware via File System Access API: DeepSeek

Browser Ransomware via File System Access API: DeepSeek

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 Check Point Research

Check Point Research demonstrates how DeepSeek's lower refusal rates allowed researchers to transform an LLM-hallucinated malware concept into a practical browser-native ransomware technique targeting Android photo directories via the File System Access API. The attack requires no native payload, APK installation, or root access — only social engineering to obtain a legitimate browser permission prompt. This research highlights how frontier AI models with weaker safety controls can independently design novel attack paths not yet seen in real-world campaigns.

Phantom Squatting: LLM Hallucinations Enable Domain Takeover

Phantom Squatting: LLM Hallucinations Enable Domain Takeover

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Dark Reading

Researchers have identified a novel attack vector dubbed 'Phantom Squatting', in which LLMs consistently hallucinate plausible but non-existent web domains for legitimate brands, which attackers can then register and weaponise. Unlike traditional typosquatting, these hallucinated domains carry implicit trust because they originate from AI-generated outputs that users and developers may act upon without verification. The technique is difficult to detect because the domains are not misspellings but plausible inventions, making automated defences less effective.

Phantom Squatting: LLM Hallucinations in Supply Chain

Phantom Squatting: LLM Hallucinations in Supply Chain

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 Palo Alto Unit 42

Unit 42 researchers have documented 'phantom squatting', a novel attack vector where adversaries register domains that LLMs consistently hallucinate when responding to developer queries, intercepting traffic from AI-assisted workflows. Analysis of 913 brands across 685,339 URL queries uncovered 13,229 confirmed malicious URLs and approximately 250,000 unregistered hallucinated domains still available for adversarial pre-registration. A concrete case study reveals a fully operational phishing kit, Montana Empire, built with an AI coding assistant and deployed against a domain Unit 42 had flagged as high-risk 23 days prior.

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