As strikes continue on Iran’s nuclear facilities, the real danger isn’t the explosion, but what happens if critical safety systems fail—and how that risk could spread across the Gulf.
Here is a draft for a Reddit post tailored for the r/homelab community.
Title: [Project] Turning a Raspberry Pi into a "Poor Man's" Enterprise IDS/NSM using Zeek and Suricata
Hey everyone,
I’ve been looking for ways to get better visibility into my network traffic without dropping $500+ on dedicated hardware or running a power-hungry 1U server 24/7. I came across this guide from HookProbe that breaks down how to deploy Zeek and Suricata on a Raspberry Pi (specifically optimized for the Pi 4/5), and I thought it would be right up this sub's alley.
The "Double Whammy": It uses Suricata for signature-based detection (finding the "known bad") and Zeek for high-level metadata/network analysis (the "context"). Usually, running both on a Pi would kill the CPU, but the post goes into some decent optimization tricks.
Resource Management: It covers pinning network interface interrupts to specific cores and increasing ring buffer sizes so you don't drop packets when your 1Gbps fiber actually hits its peak.
Edge Defense: Instead of just monitoring your "main" server, the idea is to place these at the "edge" (connected to a mirror/SPAN port on your switch) to see everything—IoT devices, guest Wi-Fi, etc.—before it even hits your core network.
The Setup: The guide walks through the /etc configurations for both tools. If you’re like me and love structured logs (DNS queries, SSL handshakes, HTTP headers) for your ELK stack or Grafana dashboards, Zeek is a goldmine.
Some questions for the community:
Is anyone else running Zeek/Suricata on ARM hardware? How are you handling the heat/throttling during heavy traffic?
Are you using a managed switch with a SPAN port, or are you using a hardware tap to feed the Pi?
For those using the Pi 5, have you noticed a significant jump in PPS (packets per second) handling compared to the Pi 4?
I’m planning to set this up this weekend to feed into my local SOC dashboard. If you're looking for a low-cost way to move past "just a basic firewall," this seems like a solid weekend project.
Curious to hear if anyone has tried a similar "Edge Security" approach!
Researcher Yarden Porat (Cyata) disclosed a vulnerability chain in CrewAI, the widely-used Python multi-agent framework. CERT/CC advisory VU#221883. No full patch released yet.
The chain:
CVE-2026-2275 — Code Interpreter silently falls back to SandboxPython when Docker is unavailable. SandboxPython allows arbitrary C function calls → RCE.
CVE-2026-2287 — CrewAI does not continuously verify Docker availability during runtime. An attacker who triggers the fallback mid-execution lands in the vulnerable sandbox.
CVE-2026-2285 — JSON loader tool reads files without path validation. Arbitrary local file read.
CVE-2026-2286 — RAG search tools don't validate runtime URLs → SSRF to internal services and cloud metadata endpoints.
Attack entry point: prompt injection against any agent with Code Interpreter Tool enabled. The attacker doesn't need code execution access to the host — they just need to reach the agent with crafted input.
Scope: Any CrewAI deployment running Code Interpreter Tool where Docker is not guaranteed to be available (or can be disrupted). Default "unsafe mode" config is fully exposed.
Current status: CrewAI maintainers are working on mitigations (fail closed instead of fallback, block C modules, clearer warnings). Not released. No CVSSv3 scores published yet.
Has anyone tested whether the Docker availability check can be disrupted mid-execution in a containerized deployment, or does that attack path require an already-degraded environment?
On March 24, 2026, Mercor AI was reportedly affected by a breach linked to the hacking group Lapsus$. The incident is believed to have originated from a supply chain attack involving a compromised LiteLLM package, which may have been inadvertently pulled by one of Mercor’s AI agents.
Through this vector, attackers allegedly gained access to internal systems, including Tailscale VPN credentials, and exfiltrated approximately 4TB of data. The leaked data reportedly included 211GB of candidate records, 939GB of source code, and around 3TB of video interviews and identity documents.
In a public statement on X (formerly Twitter), Mercor said that it had identified itself as one of many companies impacted by the LiteLLM supply chain attack. The company added that its security team acted quickly to contain the breach and begin remediation efforts. Possible attack chain pathway linked.
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