A developer-focused teardown of why shell-capable AI agents collapse prompt-injection trust boundaries, grounded in the reported DeepSeek-agent campaign against 200+ organizations and the concrete isolation and sandboxing controls that actually hold.
This post examines how attackers are chaining Claude, Qwen, and DeepSeek into agentic workflows for real-world cyberattacks and what defenders should watch for in multi-model abuse.
A practical security guide for developers on auditing MCP servers for internet exposure, leaked AI assistant credentials, and unauthenticated endpoints in response to active scanning campaigns.
This post examines how prompt injection can be chained into malicious payloads inside LLM API integrations, and how to audit agentic workflows for ransomware risk before attackers exploit them.
A deep technical walkthrough of how the OpenClaw phishing simulation exposed sensitive credential leakage in an AI agent workflow, and how developers can test and harden tool authorization boundaries against similar attacks.
A deep technical walkthrough of Cisco’s blueprint for defending LLM agents against prompt injection, tool abuse, and data exfiltration in real-world AI deployments.
A practical deep-dive on how to stress-test your app against emerging AI-assisted threats, using the threat patterns highlighted by recent reporting on Claude Mythos and GPT 5.4-Cyber.
A deep technical walkthrough of Marimo CVE-2026-39987 and how attackers chained the initial exploit with an LLM agent for post-exploitation, privilege discovery, and defensive detection opportunities.
A technical walkthrough of how red teaming an LLM differs from a traditional web app pentest, focusing on model-specific attack surfaces like prompt injection, jailbreaks, and AI security evaluation workflows.
An overview of how red teaming language models helps uncover blind spots, risks, and failure modes in LLM-powered applications.
An overview of how to design trust boundaries and defensive controls to reduce prompt injection risk in LLM-powered systems.
Explores how long-term memory in LLM applications can create overlooked security and privacy risks, and why it should be treated as a critical attack surface.
A practical guide to auditing LLM access control in multi-tenant applications to reduce data leakage, enforce tenant isolation, and strengthen security governance.