libssh2 Pre-Auth RCE Hits curl, Git, and PHP via SSH Handshake

JADEPUFFER earns attention for its entry points: Langflow (CVE-2025-3248) and Nacos (CVE-2021-29441). Those are exactly the tools platform and data-science teams deploy with broad network access, weak auth, and no security review, because they're "internal." The attacker ran end-to-end extortion through an LLM and encrypted a production database. Langflow now carries a second active CVE (CVE-2026-55255, CVSS 8.4). It's an auth bypass. Any authenticated user can execute another user's flows. Fixed in 1.9.1, already exploited for credential harvesting.

GitLost is a different failure class. GitHub's AI issue-handler reads public issue titles as instructions while holding private-repo context. Crafted issues made the agent leak private contents. That's a confused deputy via prompt injection. The agent has no way to distinguish legitimate users from attackers.

Both sit on the identity gap. 69% of enterprises share credentials across agents and 54% have already had an incident. A shared key hands one prompt-injected agent the union of every permission it grants. Standard IAM models deterministic actors. An autonomous agent picking resources at runtime fits neither the human nor the service-account mold.

The synthesis is uncomfortable but simple. AI/ML tooling deserves the same posture as a public API gateway: network segmentation, patch cadence, per-invocation scoped identities. Not trust-by-default.

Speculative decoding has been the obvious lever since 2022. Production kept hitting the same wall. Sequential drafters like EAGLE-3 can't parallelize. Parallel drafters like DFlash throw away inter-token dependencies. DSpark uses DFlash as a parallel backbone plus a lightweight Markov correction head that recovers token-to-token dependencies in one sequential pass. The cost is 0.2-1.3% latency. Acceptance rates climb 27-31% over EAGLE-3 and 16-18% over DFlash across Qwen3-4B/8B/14B. I'd want to reproduce the acceptance numbers before trusting the top of that range, but the mechanism is sound.

The nonlinearity is the part that matters. At conservative per-user speeds (80 tok/s Flash, 35 tok/s Pro), DSpark yields ~51-52% total throughput gain. Real, not dramatic. At 120 tok/s Flash / 50 tok/s Pro, gains jump to 661% and 406%. So the value tracks your SLA target, not the technique in the abstract. Read the tok/s line in your config before you read the headline. Checkpoints are MIT-licensed on HuggingFace, and the gains hold on Gemma4-12B, which is a non-DeepSeek architecture. That's the evidence this transfers rather than overfitting one model family.

Set this against the rest of the inference stack. Unsloth's NVFP4 quants hit 17,561 tok/s on B200 and fit a 27B model in 24GB VRAM. Cohere open-sourced hardware-aware dynamic speculative decoding in vLLM, which removes the manual tuning loop. That tuning loop was the actual reason most teams left speculative decoding off in prod. If you wrote off self-hosting as too slow or too expensive a quarter ago, those numbers no longer hold. The counterweight, and it's the one today's contrarian raises, is that open-weight supply itself may be tightening.