ai-security

Agent Security: Enterprise Adoption Patterns

Enterprise agent deployments have moved past pilot phase. The security patterns that have survived contact with production look different from the ones the industry was selling a year ago.

Claude Code Coding Agent: Security Posture Review

A working review of Claude Code's security posture, sandboxing model, and the practical controls enterprises need to deploy it safely at scale.

Cross-Package Analysis: Griffin AI vs Mythos

Real exploits cross package boundaries. Griffin AI's graph follows them; Mythos-class tools often stop at the file they are reading.

AI Model Weight Tampering Detection Techniques

Weight-level tampering leaves cryptographic and statistical fingerprints. Here is what current research says about detecting a modified checkpoint before it reaches inference.

Sandboxing LLM Agent Code Execution: Patterns

If your agent can execute code, something it reads from the internet can execute code. Pick your sandbox before the agent picks one for you.

MCP Server Capability Declaration Audit

An MCP server tells the world what it can do through its capability declaration. Auditing those declarations catches drift, tool poisoning, and misconfiguration before an agent gets talked into using the wrong one.

Black Hat USA 2025: Supply Chain Security Recap

Black Hat USA 2025 highlighted AI-generated code risks, build system attacks, and the maturation of SBOM tooling. Here is what mattered for supply chain teams.

Griffin AI vs AI21 Jurassic for Security Workflows

Enterprise RAG Security Rollout Antipatterns

Retrieval-augmented generation systems are where enterprise AI meets enterprise data, and where most security rollouts stumble. A catalog of the antipatterns we keep seeing.

CWE Classification Accuracy: Griffin AI vs Mythos

Getting the CWE right is not a taxonomic hobby. It drives remediation, compliance mapping, and detection engineering. Here is how grounded and pure-LLM scanners compare.

Griffin AI vs Qwen for Code Security

Qwen's open-weight models have strong code benchmarks. We dig into how they compare to Griffin AI when the workflow is real code security, not just leetcode.

SecBench Methodology Reviewed

SecBench positioned itself as a comprehensive cybersecurity knowledge and reasoning benchmark for LLMs. A methodology review of its construction, scoring, and the gaps that separate the advertised coverage from what the benchmark actually exercises.