AI research company i14 partnered with AWS to port its Laplace 3B model, a 24-layer architecture built on Gated Linear Attention (GLA) instead of standard transformer attention, onto AWS Trainium2 using native PyTorch (TorchNeuron Native). Rather than a KV cache that expands with every token, the model keeps a constant 29.5 MB recurrent state per sequence. On a single trn2.48xlarge instance, the team reached about 120,000 training tokens per second and served 11,544 output tokens per second across 1,280 simultaneous sequences through an unmodified vLLM engine.
CISA has confirmed real-world exploitation of six vulnerabilities and placed them on its Known Exploited Vulnerabilities Catalog. Four of the six hit Plex Media Server, covering two SSRF bugs, a URI-scheme handling flaw and a file-path control issue; the remaining two are an OS command injection in EG Secure Solutions SiteGuard Server Edition and an uncontrolled search path flaw in Siemens Software Center. Because the flaws are already being exploited, Binding Operational Directive 26-04 forces federal civilian agencies to fast-track fixes on any exposed system that could hand attackers full control, and to check for prior compromise first.
A joint advisory from the FBI, CISA, USSS and European law-enforcement agencies details the SafePay ransomware group, active since August 2024 and responsible for at least 600 attacks across North America, South America, Europe and Australia. SafePay gains access via stolen/brute-forced credentials, phishing, and exploitation of known VPN and edge-device vulnerabilities, then uses legitimate admin tools to move laterally, disable defenses, exfiltrate data and encrypt files with a ChaCha20/RSA hybrid scheme before demanding ransom.
AWS introduced Agent Registry, a managed catalog service for Model Context Protocol (MCP) servers, agents, and agent skills. Instead of hardcoding every tool into an orchestrator, agents can now search an approved, versioned catalog at runtime and build a client from the returned descriptor. A reference sample demonstrates a Discover → Instantiate → Execute loop using Amazon Bedrock AgentCore Gateway and AgentCore runtime, with Anthropic Claude Sonnet as the foundation model.
Eon, a data protection ISV running on AWS, replaced per-tool credentials and local MCP servers with a single Amazon Bedrock AgentCore MCP Gateway. The gateway uses Cedar-based policies to enforce least-privilege access for both human engineers and an autonomous SRE agent running Claude on Amazon Bedrock, routing calls to MCP servers hosted on AgentCore Runtimes across multiple AWS accounts. Developer onboarding time dropped from about two days to about two minutes after rolling the system out to 100+ developers and roughly 10,000 sessions.
AWS and Keyfactor published a reference design that fixes a known weak point in Model Context Protocol (MCP) deployments: MCP has no built-in way to authenticate which agent is actually making a request. The architecture adds three trust layers — user-to-system, agent-to-agent, and agent-to-tool-server — built on signed request envelopes, delegation assertions signed by the user, RFC 8693 audience-restricted tokens, and live OCSP revocation checks. A working example runs on Amazon Bedrock and the AWS Strands Agents SDK with Keyfactor EJBCA issuing and validating the X.509 certificates, and the full code is on GitHub.
AWS has rolled out structured outputs across every Anthropic Claude model hosted on Amazon Bedrock, reaching both the standard runtime endpoint and the newer Mantle endpoint built for agentic workloads. Instead of hoping the model returns valid JSON, developers can now lock responses to an exact schema through constrained decoding, which blocks the model from generating tokens that would break the format. A companion feature, strict tool use, applies the same guarantee to function-calling arguments so agent workflows don't fail on malformed inputs.
AWS has rolled out Pegasus 1.5, a video-language model built by TwelveLabs, as a fully managed option inside Amazon Bedrock. Unlike earlier versions that answered one question per clip, this release scans an entire video and outputs a JSON list of every moment matching criteria you define, complete with start and end times. It also handles longer recordings — up to two hours in one call — and lets you feed in a video straight from an S3 bucket or as a base64 string, skipping any indexing step.
AWS and Deepgram jointly rebuilt the Nova-3 speech-to-text model to run natively on Trainium2 chips instead of standard GPU-based PyTorch setups, targeting large offline transcription jobs. The work involved rewriting bottleneck layers as custom kernels and replacing Deepgram's Python serving layer with a Rust runtime calling Trainium directly. The result: a single trn2.3xlarge instance running eight parallel model copies delivers 11-70% more transcribed audio per dollar than Deepgram's prior infrastructure, with similar savings potentially available to customers self-hosting on SageMaker.
AWS released a push-based NIXL connector for vLLM that changes how key-value (KV) cache data moves between prefill and decode instances in disaggregated LLM serving. Instead of the decode instance pulling KV blocks after prefill finishes, the decode instance registers its target memory in advance and the prefill instance writes the KV data directly once ready, moving coordination work off the critical path. Benchmarked on a 1-prefill/1-decode Qwen3-32B deployment using EC2 P5en instances with EFA, push mode delivered up to 3x better time-to-first-token (TTFT) and up to 30% better time-per-output-token (TPOT) than the default pull mode, especially under high request rates.
AWS tested its AWS Security Agent (now part of AWS Continuum) against a freshly built benchmark of 37 real, recently disclosed CVEs, each run as a live containerized target with a hidden flag. Starting from a low-privilege endpoint with only a one-line hint and no proof-of-concept, the agent, running on Claude Opus 5, captured 33 of 37 flags through genuine end-to-end exploitation after an integrity audit, with 2 captures reclassified as unintended-path solves and 2 genuine misses where the agent broke the target before extracting the flag. AWS built this benchmark to avoid the contamination problem of public CVE suites, which models may have memorized from training data.
AWS published a governance framework called Trust in Depth, designed to let AI agents operate with more autonomy while staying within enforceable limits. It combines scoping agent permissions before deployment, giving each agent a verifiable identity, enforcing boundaries at runtime via policy gateways, and observing agent behavior afterward. The framework leans on automated reasoning (mathematical proof) for rules that can be precisely defined, and human judgment for ambiguous cases, using AWS tools like Bedrock AgentCore, Guardrails, Kiro, Strands, Agent Registry and CloudWatch Omni.