Cribl has unveiled a new SIEM and an enterprise AI gateway at CriblCon, targeting cost overruns in both security operations and AI inference.

The Cybersecurity Pulse highlights major developments in AI security, SIEM innovation, and incident response, including NVIDIA’s Open Agent Safety Platform, Cribl’s Detect, Microsoft’s Defender ISOC preview, and OpenAI’s agent safety work. It also covers active threat activity from ShinyHunters, JADEPUFFER, and Citrix NetScaler exploitation, along with new defenses from ZeroDrift, Vega, Reco, and Rig Security. #ShinyHunters #JADEPUFFER #OpenAgentSafetyPlatform #CriblDetect #DefenderISOC #CitrixNetScaler #OpenAI

## StreamAI Preliminary Compatibility Matrix — First External 2 GB VRAM Inference Success Quick update on the StreamAI controlled beta: we now have our first successful **external inference test on a 2 GB discrete GPU**. A beta tester, **Logos-Adri** , tested StreamAI 0.3.9.12 on an **NVIDIA GeForce GTX 1050 with 2 GB VRAM**. The engine initialized successfully and completed local Qwen2.5 1.5B inference. ### Preliminary compatibility results GPU | VRAM | System RAM | Profile | Status | Observed Performance ---|---|---|---|---|--- AMD Radeon RX 570 | 4 GB | 32 GB | Fast | **Qualified** | ~1.11 tok/s NVIDIA GeForce GTX 1050 | 2 GB | Pending tester confirmation | Ultra-Low-VRAM Fast | **Experimental PASS** | ~0.99 tok/s Intel Iris Xe | 2 GB reported | ~16 GB | Earlier test profile | **Unqualified / needs retest** | — Intel UHD 770 | 2 GB reported | ~16 GB | Earlier test profile | **Unqualified / needs retest** | — **Important:** “Unqualified” does not mean incompatible. It means that configuration does not yet have enough evidence to be classified as qualified. Similarly, I’m deliberately calling the GTX 1050 result **Experimental PASS** , not Qualified, until the structured qualification suite is completed. ### Why the GTX 1050 result is interesting This is a direct before/after test on the **same 2 GB machine**. **StreamAI 0.3.9.11** * Clean-machine provisioning: **PASS** * Automatic model download: **PASS** * Model/cache preparation: **PASS** * Engine initialization: **FAIL — insufficient GPU memory** * Inference: **not reached** The failure occurred while attempting an approximately **445 MiB GPU allocation**. Rather than classifying the GTX 1050 as incompatible, I used that failure to create a separate experimental low-VRAM execution profile in StreamAI 0.3.9.12. **StreamAI 0.3.9.12** * GTX 1050 detected: **PASS** * VRAM detected: **2.0 GB** * DirectML: **Ready** * Low-VRAM profile selected: **PASS** * Engine initialization: **PASS** * Local inference: **PASS** Two external generations produced approximately: **0.9852 tok/s — TTFT 1.9166 s** **0.9926 tok/s — TTFT 1.6131 s** The tester successfully generated complete responses locally, including the standard test prompt: `Explain how a black hole forms.` ### 4 GB regression test Before sending 0.3.9.12 externally, I also tested it on the existing **Radeon RX 570 4 GB qualification machine**. StreamAI retained its existing qualified Fast configuration and completed inference at: **1.1087 tok/s — TTFT 1.0677 s** So the new experimental 2 GB path did not require replacing or weakening the existing qualified 4 GB path. ### What happens next The GTX 1050 tester is now running StreamAI’s structured qualification suite. I’m looking for more than a successful screenshot before calling the profile qualified. The qualification results will give us standardized evidence for repeated generation, context handling, determinism/lifecycle behavior, and the machine/runtime configuration. If those tests pass, the matrix will be updated from: **GTX 1050 2 GB — Experimental PASS** to: **GTX 1050 2 GB — Qualified** If something fails, I’ll publish that result too. ### Publishing failures Several people here suggested publishing unsuccessful configurations as well as successful ones. I agree. Going forward, the compatibility matrix will distinguish: **Qualified** — completed the defined qualification suite **Experimental PASS** — actual inference completed; qualification pending **Unqualified / needs retest** — insufficient evidence or qualification wasn’t completed **Failed** — a specific tested configuration failed, with the failure stage recorded This distinction matters because a failed configuration doesn’t necessarily mean the hardware itself is incapable of running StreamAI. The GTX 1050 is a good example: the earlier configuration failed, while a subsequent memory-bounded profile successfully reached inference on the same hardware. Future matrix entries will include as much verified information as practical: GPU, VRAM, system RAM, StreamAI version/profile, context, TTFT, sustained tokens/sec and failure stage. There is **no automatic telemetry collection** for these beta tests. Qualification results remain local and testers choose whether to return them. ### More testers welcome I’m still looking for Windows users interested in testing StreamAI on low-VRAM hardware, especially: * NVIDIA 2–4 GB GPUs * AMD 2–8 GB GPUs * Intel Arc / Iris Xe / UHD graphics * laptop GPUs * integrated graphics * older or unusual DirectML-capable hardware Windows 10/11 and a DirectML-capable GPU are the primary requirements. **16 GB+ system RAM is preferred** , although lower-memory systems are also useful for establishing the actual operating boundaries. The objective is not simply to collect successful runs. I want to establish, with reproducible real-hardware testing, **how far local LLM inference can be pushed when GPU memory is the primary constraint**. A big thank-you to **Logos-Adri** for testing both builds. The original GTX 1050 failure provided the information that led directly to the experimental 2 GB profile and this successful inference result. I’ll update the matrix again when the structured GTX 1050 qualification results are available.

Hi everyone, I’ve been developing a Windows local-LLM application called **StreamAI** , specifically focused on running useful language models on systems with limited GPU memory. My development machine is deliberately modest: **AMD Radeon RX 570 — 4 GB VRAM** **32 GB system RAM** On that system, the current beta has qualified two modes: **Fast mode** Qwen2.5 1.5B Instruct About **1.1 tokens/sec** **Quality mode** Qwen1.5 7B Chat About **0.45 tokens/sec** The goal of the project is not to compete with high-end GPUs. I’m trying to determine how useful local LLM inference can remain on older or limited-VRAM Windows hardware. StreamAI uses a memory-bounded CPU/GPU streaming approach so that model execution does not depend on keeping the entire working model resident in GPU memory. I have reached the point where testing only on my own RX 570 is no longer useful. I am looking for a small number of Windows testers with different hardware. I am especially interested in: * AMD GPUs with 2–8 GB VRAM * older Radeon cards * NVIDIA GPUs with 2–8 GB VRAM * Intel Arc or Intel integrated graphics * laptops with limited or shared GPU memory * machines that normally struggle with local LLM runtimes I would initially like to test on roughly **6–10 different machines** rather than distribute the beta widely. The beta includes an automated hardware and inference qualification process. After testing, it creates a small results ZIP that the tester can inspect and manually send back to me. **There is no automatic telemetry or automatic uploading of results.** The beta is currently a compiled Windows application and uses a short-lived machine-bound tester license while I keep the test group controlled. If you are interested, please reply with: `GPU / VRAM / System RAM / Windows version` For example: `GTX 1650 / 4 GB / 16 GB RAM / Windows 11` I am interested in failures just as much as successes. My goal is to eventually publish the hardware results, performance measurements, and practical limits so the information is useful to other people working with constrained hardware. The main question I am trying to answer is: **How low can the hardware requirements go before local LLM inference stops being genuinely usable?** If there is interest, I’ll share the results from the different machines here as the testing progresses

Hi everyone, I’ve been developing a Windows local-LLM application called **StreamAI** , specifically focused on running useful language models on systems with limited GPU memory. My development machine is deliberately modest: **AMD Radeon RX 570 — 4 GB VRAM** **32 GB system RAM** On that system, the current beta has qualified two modes: **Fast mode** Qwen2.5 1.5B Instruct About **1.1 tokens/sec** **Quality mode** Qwen1.5 7B Chat About **0.45 tokens/sec** The goal of the project is not to compete with high-end GPUs. I’m trying to determine how useful local LLM inference can remain on older or limited-VRAM Windows hardware. StreamAI uses a memory-bounded CPU/GPU streaming approach so that model execution does not depend on keeping the entire working model resident in GPU memory. I have reached the point where testing only on my own RX 570 is no longer useful. I am looking for a small number of Windows testers with different hardware. I am especially interested in: * AMD GPUs with 2–8 GB VRAM * older Radeon cards * NVIDIA GPUs with 2–8 GB VRAM * Intel Arc or Intel integrated graphics * laptops with limited or shared GPU memory * machines that normally struggle with local LLM runtimes I would initially like to test on roughly **6–10 different machines** rather than distribute the beta widely. The beta includes an automated hardware and inference qualification process. After testing, it creates a small results ZIP that the tester can inspect and manually send back to me. **There is no automatic telemetry or automatic uploading of results.** The beta is currently a compiled Windows application and uses a short-lived machine-bound tester license while I keep the test group controlled. If you are interested, please reply with: `GPU / VRAM / System RAM / Windows version` For example: `GTX 1650 / 4 GB / 16 GB RAM / Windows 11` I am interested in failures just as much as successes. My goal is to eventually publish the hardware results, performance measurements, and practical limits so the information is useful to other people working with constrained hardware. The main question I am trying to answer is: **How low can the hardware requirements go before local LLM inference stops being genuinely usable?** If there is interest, I’ll share the results from the different machines here as the testing progresses

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