Honeypot

Apate Launches AI Agents That Infiltrate Scam Networks for Intelligence

FIRST LOOK ATLAS OWASP MEDIUM ▲ 7.8 TechCrunch AI Oct 15, 2026

Apate deploys thousands of AI agents that impersonate potential victims across calls, messages, emails, and social platforms to engage fraudsters and extract operational intelligence — including bank accounts, crypto wallets, malicious domains, and impersonated brands. This closes a longstanding gap in proactive fraud intelligence: defenders have historically only learned scammer infrastructure details after real victims were harmed, whereas Apate surfaces those indicators pre-victimisation at scale. Residual gaps remain around intelligence freshness pipelines, integration maturity with existing SIEM and fraud platforms, and the coverage breadth across emerging scam vectors like AI-generated deepfake channels.

AI Agent Builds Self-Expanding Stolen LLM Inference Supply Chain

ATLAS OWASP HIGH ▲ 9.2 SANS Internet Storm Center Sep 12, 2026

A researcher operating an AI honeypot captured a semi-autonomous coding agent conducting a full-cycle offensive operation: locating poorly secured LLM resale gateways, harvesting API credentials via web vulnerabilities, validating stolen inference capacity, and aggregating it behind an attacker-controlled unified gateway. The operation is notable not for novel individual techniques but for its feedback loop architecture — stolen inference capacity is used to fund and expand further credential theft, creating a partially self-sustaining supply chain. The honeypot inadvertently received ~43 KB of the agent's control-plane data, including its AGENTS.md playbook, collected API keys, reconnaissance scripts, and the operator's unproxied egress IP.

Context Bombing Uses Prompt Injection to Stop AI Hacking Agents

ATLAS OWASP MEDIUM ▲ 7.8 Schneier on Security Aug 14, 2026

Researchers at Tracebit have demonstrated a defensive technique called 'context bombing,' which plants prompt injections alongside cloud secrets on AWS to halt AI-driven attack agents by triggering their own guardrails. The approach reportedly reduced admin escalation attempts from 57% to 5% in testing, representing a novel inversion of the prompt injection threat. However, the technique's effectiveness is limited to LLMs with active guardrails, leaving a growing class of ungoverned, locally-run models unaffected.

Cisco AI Agents Vulnerable to Prompt Injection Honeypots

ATLAS OWASP MEDIUM ▲ 7.2 Cisco Talos Apr 30, 2026

Cisco Talos researcher Martin Lee demonstrates how generative AI can be used to rapidly deploy adaptive honeypot systems that deceive and study AI-driven attack agents. The technique exploits a fundamental weakness in AI agents — their lack of situational awareness — causing them to interact with simulated vulnerable systems as if they were real targets. This defensive approach shifts the paradigm from passive detection to active manipulation, giving defenders new insight into automated threat actor methodologies.

CVE-2026-33626: LMDeploy SSRF Exploited in 13 Hours

ATLAS OWASP CRITICAL ▲ 9.2 The Hacker News Apr 25, 2026

A critical SSRF vulnerability in LMDeploy (CVE-2026-33626), an open-source LLM deployment toolkit, was actively exploited within 13 hours of public disclosure, with attackers using the vision-language image loader to probe cloud metadata services, internal networks, and exfiltrate data. The attack pattern demonstrates that AI inference infrastructure is being weaponised at speed comparable to traditional CVE exploitation cycles, with no PoC required. This incident reinforces a broader trend of threat actors treating LLM-serving infrastructure as high-value lateral movement targets.

Scanning for AI Models, (Tue, Apr 14th)

ATLAS OWASP HIGH ▲ 7.8 SANS Internet Storm Center Apr 15, 2026

A single threat actor (IP 81.168.83.103) has been systematically scanning internet-facing systems since at least January 2026, specifically targeting credential files, API tokens, and configuration data associated with popular AI platforms including OpenAI, Anthropic Claude, HuggingFace, and the Openclaw/Clawdbot tools. The campaign focuses on harvesting AI API credentials and secrets stored in predictable file paths, representing a targeted reconnaissance effort against AI model deployments. If successful, these probes could enable API key theft, model access abuse, and broader compromise of AI-integrated systems.