Palo Alto Networks Integrates Prisma AIRS with OpenAI Codex for AI Security

SynapseCommand® adapters are now open source. We have released the interoperability layer of SynapseCommand under the Apache 2.0 licence, including the Canonical Data Model, JSON Schema, adapter SDK, validation harness and 10 verified adapters. Supported formats include ASTERIX, STANAG 4676, STANAG 4607, TAK, AIS, ADS-B, PNTMAP and Picogrid. For 10 connected systems, the canonical-model approach reduces the integration problem from 45 point-to-point translations to 10 adapters. More importantly, partners, primes and procurement authorities can now inspect and test our interoperability layer independently. The contract is open. The product remains proprietary. #SynapseCommand #OpenSource #DefenceTech #Interoperability #NATO #C2

We’ve opened the SynapseCommand® integration layer. Today, Decent Cybersecurity released the interoperability layer at the heart of SynapseCommand as open source under the Apache 2.0 licence. The release includes: • SynapseCommand Canonical Data Model v1.0.0 • Published JSON Schema • 10 verified adapters • Adapter SDK • Validation harness • 2,400+ automated tests Supported integrations include PNTMAP, TAK, AIS, ADS-B, Picogrid, ASTERIX CAT021, CAT048 and CAT034, STANAG 4676 and STANAG 4607. Why open it? Because interoperability should be independently verifiable. Partners, primes, system integrators and procurement authorities can now inspect the data contract, validate implementations and build new integrations without waiting for an NDA cycle or relying on proprietary claims. The architecture is intentionally simple: External systems → adapters → Canonical Data Model → SynapseCommand Instead of maintaining N(N-1)/2 point-to-point translations, each system needs one adapter into the common contract layer. For 10 systems, that means 10 adapters rather than 45 discrete translations. The boundary is equally important: The contract is open. The product remains proprietary. Fusion, correlation, analysis, operational logic and the SynapseCommand Warfighting Agent Suite remain inside the proprietary platform. The public layer is restricted to translation and interoperability. Five binary-format implementations are also tested for byte-exact round trips, while the repository includes automated structural checks, mutation testing and contribution governance. Next on the roadmap: ASTERIX CAT062 and CAT023. Repository: link in the full announcement Full announcement: https://lnkd.in/duJbPhvr We want partners to be able to verify interoperability before they have to trust us. #DefenceTech #OpenSource #Interoperability #NATO #ASTERIX #STANAG #CommandAndControl #C2 #MilitaryAI #SovereignAI #SynapseCommand

Excited to share my latest project: Aegis Gateway — a high-performance, Zero-Trust Privacy Gateway for local LLMs! When bridging AI capabilities with strict data governance, you cannot rely on beginner-friendly abstractions. Aegis sits directly in the request path, masking and rehydrating PII in real-time while maintaining near-zero latency. Key Highlights: Zero-Trust Data Sanitization: Microsoft Presidio integration for dynamic PII tokenization. High-Throughput Middleware: Built directly on raw ASGI primitives to bypass standard middleware latency bottlenecks. Containerized Microservices: Fully isolated Docker stack orchestrating FastAPI, Redis, and an offline Ollama inference engine. Enterprise Observability: Structured JSON audit logging using Loguru for SIEM integration. Automated CI/CD: GitHub Actions integration pipeline running headless testing with tinyllama. Check out the code and architecture breakdown here: 👉 https://lnkd.in/d6cdD2Ta #SystemsEngineering #FastAPI #Docker #DevOps #Cybersecurity #LLM #AppSec

An AI agent does not need root access to create a serious Linux incident. A service account with repository write access, a CI token, and permission to trigger deployments may have more operational power than a traditional privileged user. In modern DevSecOps environments, identity and workflow authority often matter more than UID 0. That makes AI governance an access-control problem as much as a model-safety problem. The blast radius is defined by tokens, API scopes, trusted automation, and which actions are accepted without human review. In practical terms, it is a good time to: • enumerate tokens and SSH keys available to AI service accounts, build runners, and automation users • review sudoers rules, repository permissions, and CI/CD roles for wildcard or administrative privileges • compare container and Kubernetes service-account permissions with the actions agents actually require • identify deployment workflows that accept AI-generated changes without signed commits, protected branches, or manual approval The useful takeaway is simple: constrain the identity before debating the intelligence. More detail: #LinuxSecurity #AccessControl #DevSecOps #KubernetesSecurity #OpenSourceSecurity https://lnkd.in/ea5M4Fbc

The open-source debate misses the control that matters most: who can authorize real-world actions. Model access is only one layer. Production risk lives in shell access, deployment rights, secrets, and trusted automation. In practical terms, it is a good time to: • inventory AI workloads with code-execution or deployment privileges • compare repository and Kubernetes permissions with documented requirements • revoke stale credentials and require approval for high-impact actions More detail: #OpenSourceSecurity #DevSecOps #AccessControl #LinuxSecurity https://lnkd.in/ea5M4Fbc

Critical Agentic AI Breach: Indirect Prompt Injection in GitHub Model Context Protocol (MCP) Leads to Autonomous Cross-Repo Exfiltration Security research and exploit disclosures surrounding Model Context Protocol (MCP) integrations in developer AI agents reveal a critical vulnerability where indirect prompt injection allows attackers to hijack local AI agents and autonomously exfiltrate private code repositories and cryptographic secrets. Technical Breakdown: • Technical Root Cause: Absence of trust boundary segmentation within the Machine/Model Context Protocol (MCP) and agent execution loops, where user-supplied content (public GitHub Issues/PRs) is treated with the same trust level as system instructions. • Detailed Attack Vector: Adversaries place adversarial prompt payloads inside public repository Issues; when a developer triggers a local AI coding agent (e.g., Claude Code, Cursor, Copilot Workspace) to analyze or triage the issue, the agent parses the hidden commands, leverages its elevated user GitHub OAuth token, and systematically reads private enterprise repositories to exfiltrate tokens via out-of-band HTTP requests. • Technical Impact: Autonomous cross-repository data theft, unauthorized private source code exfiltration, credential leakage (API keys, SSH keys in CI variables), and potential supply-chain poisoning via malicious agent-initiated pull requests. Strategic Takeaway for CISOs & Architects: Autonomous AI agents operating with broad, global OAuth permissions on developer endpoints represent untamed lateral movement channels; enterprise agentic toolkits must enforce fine-grained scoped permissions and strict boundary isolation between untrusted input text and privileged tool execution. Immediate Action Items & Hardening: • Restrict GitHub personal access tokens (PAT) and OAuth scopes assigned to AI agent environments to the strict minimum necessary repository paths (enforce Read-Only permissions for untrusted triage tasks). • Deploy deterministic token-sanitization proxies and guardrail filters on MCP tool-calling servers to intercept out-of-band exfiltration attempts and block dynamic execution of embedded prompt-control tags. • Configure SIEM and developer endpoint monitoring (Sysmon Event ID 1 / Auditd / GitHub Audit Logs) to detect anomalous bursts of cross-repository clone/read activity triggered by local AI agent processes. #CyberSecurity #AISecurity #ModelContextProtocol #MCP #IndirectPromptInjection #LLMSecurity #AgenticAI #AppSec #CISO #ThreatIntelligence #DevSecOps #Hardening

A critical vulnerability in IBM-owned, low-code AI builder Langflow lets unauthenticated attackers execute code remotely on vulnerable default deployments, potentially putting organizations running those instances at immediate risk. https://lnkd.in/dkQw3ACq?

🚀 AI Agent Security & Control Plane: Introducing MCP SafeGate (Open Source)! As AI agents and LLM integrations rapidly move into production environments, enterprise security, PII data leakage, and operational governance have become paramount challenges. To address these needs, I built AI Governance Suite / MCP SafeGate—an ultra-low latency, Rust-powered reverse proxy control plane. Key Features & Benchmark Performance: 🛡️ PII Eraser Engine: Automated masking of sensitive data (API Keys, Credit Cards, Bearer Tokens) via Shannon Entropy, Regex, and Luhn Algorithm (~1.8 µs scan latency). ⚡ Dynamic WASM Policies: Tenant-level WebAssembly policy routing in multi-tenant architectures (<1 µs routing overhead). 🔐 HMAC-SHA256 Audit Logging: Non-blocking, cryptographically signed, and immutable audit logs. 🚨 CircuitBreaker & Outlier Isolation: Automated quarantine for anomalous and policy-violating agents. 🐳 Production-Ready & Hardened: Multi-stage Docker setup, rootless (unprivileged user) security hardening, and GitHub Actions CI/CD automation. 🧪 Chaos & Stress Tested: Successfully processed 200 high-throughput requests across 20 concurrent tasks in 80 ms with zero panics or deadlocks. 🔗 Open Source GitHub Repository: https://lnkd.in/dttB4WpW #Rust #SystemArchitecture #AIGovernance #CyberSecurity #WebAssembly #OpenSource #DevOps #LLM #SoftwareEngineering

📌What is MCP SafeGate in a Nutshell? When AI agents interact with company databases or APIs, they risk accidentally exposing secrets, credit cards, or sensitive data. MCP SafeGate acts as a "smart security guardrail" (Reverse Proxy) sitting between the AI agent and external services. Scans outgoing agent payloads in sub-millisecond latency to automatically mask confidential info. Instantly quarantines agents that exhibit abnormal or policy-violating behavior. Built ground-up in Rust to ensure zero performance bottleneck. 🔗 Open Source GitHub Repository: https://lnkd.in/dttB4WpW #Rust #SystemArchitecture #AIGovernance #CyberSecurity #WebAssembly #OpenSource #DevOps #LLM #SoftwareEngineering

Your .env file isn't the only place you should worry about secrets. Developers have spent years learning not to expose credentials in code. We use: Secret managers Environment variables Kubernetes secrets IAM policies Key rotation But there’s a newer problem. What happens after your application retrieves the data and hands it to an AI agent? The secret may be protected at rest. But during execution, the agent can still read it, reason over it, pass it to another tool, or include it in an API request. That’s a different security problem. The next generation of application security needs to think about data exposure during execution, not just where secrets are stored. #Developers #AISecurity #AgenticAI #ApplicationSecurity #DataSecurity #Securelytix