Jailbreaks

AI Agents Generate Custom Malware in Mexico, Brazil

ATLAS OWASP HIGH ▲ 7.5 Dark Reading May 14, 2026

Two threat campaigns targeting organisations in Mexico and Brazil have leveraged AI agents to dynamically generate customised hacking tools, marking a notable escalation in automated, AI-assisted cyberattacks. The use of AI agents for on-the-fly tool generation lowers the technical barrier for attackers and accelerates the attack cycle. This represents a concrete, in-the-wild demonstration of agentic AI being exploited as an offensive capability.

GPT-5.5 and Claude Mythos Lower Barriers to Offensive AI

ATLAS OWASP HIGH ▲ 7.2 Schneier on Security May 14, 2026

The UK AI Security Institute has evaluated GPT-5.5 and found it comparable to Claude Mythos in identifying security vulnerabilities, with both models now generally available to the public. This parity raises serious concerns about the lowered barrier to entry for offensive cyber operations, as adversaries can leverage widely accessible models for vulnerability research. Commentary from security experts highlights that LLM-based vulnerability discovery is constrained to known attack patterns, but the existence of jailbreaks means guardrails provide only partial mitigation.

CrowdStrike Red Teaming: LLM Jailbreak and Data Poisoning

ATLAS OWASP MEDIUM ▲ 6.5 SecurityWeek May 06, 2026

Joey Melo, Principal Security Researcher at CrowdStrike, outlines his methodology for AI red teaming, focusing on manipulating LLM guardrails through jailbreaking and data poisoning without altering underlying source code. His work, rooted in competitive AI hacking challenges, translates classical adversarial thinking into the emerging field of machine learning security. The profile highlights the growing professionalisation of AI red teaming as organisations seek to harden LLM deployments against real-world manipulation attacks.

Flowise and n8n: Auth Bypass in Exposed LLM Services

ATLAS OWASP HIGH ▲ 8.5 The Hacker News May 06, 2026

A scan of over one million exposed AI services found pervasive security failures including absent authentication, leaked API keys, and exposed business logic across self-hosted LLM deployments. Agent management platforms such as Flowise and n8n were discovered internet-exposed without access controls, revealing credential lists and internal workflows. The findings indicate systemic misconfiguration risk as enterprises race to self-host AI infrastructure without applying baseline security practices.

Llama Guard 4 Jailbreak Detection Vulnerable to Prompt Injection

ATLAS OWASP LOW ▲ 7.2 Hugging Face Blog Apr 28, 2026

Meta has released Llama Guard 4, a 12B multimodal safety classifier designed to detect and filter unsafe content in both image and text inputs/outputs for production LLM deployments. The model addresses jailbreak attempts and harmful content generation across 14 hazard categories defined by the MLCommons taxonomy. Alongside it, two lightweight Llama Prompt Guard 2 classifiers (86M and 22M parameters) target prompt injection and prompt attack detection.

GPT-5.4-Cyber Jailbreak and Prompt Injection Risks

ATLAS OWASP HIGH ▲ 7.2 The Hacker News Apr 15, 2026

OpenAI has launched GPT-5.4-Cyber, a cybersecurity-optimised model variant, alongside an expanded Trusted Access for Cyber (TAC) programme targeting authenticated defenders and security teams. While the initiative is framed as a defensive measure, the dual-use nature of a vulnerability-detection model introduces significant risk of adversarial inversion — where threat actors could exploit the same capabilities to discover and weaponise unpatched vulnerabilities at scale. OpenAI acknowledges this risk and states it is iteratively strengthening safeguards against jailbreaks and adversarial prompt injection as access broadens.

Anthropic Claude Mythos Sparks AI Vulnerability Storm Warning

ATLAS OWASP HIGH ▲ 7.2 Dark Reading Apr 14, 2026

The Cloud Security Alliance has issued a warning about an anticipated 'AI vulnerability storm' following the release of Anthropic's Claude Mythos model, urging CISOs to prepare defensive postures in advance of expected exploit activity. The advisory signals growing institutional concern that major LLM releases create systemic risk windows as adversaries probe new model capabilities and attack surfaces. Security leaders are being advised to treat post-release periods of frontier AI models as high-alert intervals requiring elevated monitoring and response readiness.