Understanding Modern Web Security Practices
A deep dive into multi-layered defense strategies for modern web applications, from input validation to CSP.
Read article →Security Research Blog
In-depth analysis of web security, AI safety, and emerging threats from our research team.
Safe Coding and the End of Memory Safety Bugs
How the industry is eliminating memory safety vulnerabilities by writing new code in safe languages instead of rewriting legacy systems.
Read article →Latent Reasoning: The Next Frontier for LLM Capabilities
Exploring how LLMs can reason directly in latent representations, bypassing the token bottleneck entirely.
Read article →The Agent Perimeter Fallacy: Why Detection-Based AI Security Fails
Why detection-based defenses for AI agents repeat the failures of 1990s perimeter security, and what structural alternatives exist.
Read article →What the Claude Code Source Leak Actually Tells Us
Analyzing Anthropic's packaging error, the competitive implications, and what repeated incidents signal about operational maturity.
Read article →Indirect Prompt Injection Through RAG Poisoning: A Practical Analysis
How attackers embed adversarial payloads in documents indexed by retrieval-augmented generation systems to hijack LLM behavior without direct access.
Read article →MCP Security: Tool Poisoning and the Trust Boundary Problem
The Model Context Protocol enables powerful AI-tool integrations but introduces tool poisoning, rug-pull attacks, and cross-server exfiltration risks that the protocol does not yet address.
Read article →LLM Red Teaming at Scale: Automated Discovery of Jailbreak Vectors
How organizations are using adversarial ML and automated red teaming frameworks to discover jailbreak vulnerabilities in LLMs before attackers do.
Read article →Software Supply Chain Attacks in 2025: From Typosquatting to AI-Generated Malware
The software supply chain threat landscape has evolved dramatically, with attackers now using AI to generate convincing malicious packages and automate dependency confusion attacks.
Read article →Zero Trust for AI Workloads: Securing the Inference Pipeline
Applying zero trust principles to AI inference pipelines requires rethinking identity, access control, and continuous verification for model serving, data retrieval, and tool execution.
Read article →Prompt Injection Defense in Depth: Layered Strategies That Actually Work
No single defense stops prompt injection. A practical defense-in-depth approach combines input preprocessing, instruction hierarchy, output validation, and structural constraints.
Read article →CVSS Is Not Enough: Risk-Based Vulnerability Prioritization with EPSS
CVSS measures severity but not exploitability. EPSS uses machine learning to predict which vulnerabilities will actually be exploited, enabling smarter remediation prioritization.
Read article →AI Agent Tool-Use Exploits: When Function Calling Becomes an Attack Vector
As AI agents gain access to external tools and APIs, attackers can manipulate function calling to execute unintended actions, exfiltrate data, and escalate privileges.
Read article →Securing the CI/CD Pipeline: Preventing Build System Compromise
Build systems are high-value targets that can compromise every artifact they produce. Hardening CI/CD pipelines requires hermetic builds, provenance attestation, and least-privilege runners.
Read article →The Instruction Hierarchy: Teaching Models to Resist Prompt Injection
OpenAI's instruction hierarchy approach trains models to prioritize system-level prompts over user inputs, establishing a privilege model within the LLM itself.
Read article →Links in this article were last verified on March 2025.