AgentsAug 26
Image: cloud.google.com
Mandiant chains agents to hunt vulnerabilities in source code
Mandiant described the Agentic Vulnerability Discovery Harness today, built on Google's Agent Development Kit, a framework for wiring several agents together. The system works through threat modeling, then entry-point discovery, then hypothesis generation, and only escalates a finding to a person once it has been confirmed. That is a longer chain of reasoning over the same source code than an automated scanner performs, and it aims human review at findings that already carry supporting evidence.
Positive - Mandiant’s multi-agent review system searches source code for vulnerabilities while reserving confirmed findings for human validation.
AgentsAug 26
Image: github.com
SpecterOps releases Blacklight to map local AI-agent exposure
Blacklight, released by SpecterOps today, finds and explains the local attack surface (that is, the software and access an intruder could reach) created when AI agent tools get installed on a machine. It runs on Windows, macOS, and Linux and is pointed at locally deployed agent environments. Administrators who have watched employees and developers install agent tooling on their own can now take an inventory of what those installs expose.
Positive - Blacklight gives defenders a cross-platform way to identify the local systems and interfaces that AI agents could expose.
AgentsAug 26
Image: linkedin.com
AI-assisted vulnerability reports land in hours; triage is still manual
Security researcher Anshuman Bhartiya described today how he now takes a source-code flaw to a complete, verified bug-bounty submission in hours, while the programs receiving those reports still work through them by hand. His setup runs 18 vulnerability-specific detectors across source-code changes, Claude prepares most of the submission, Codex independently checks the claims, and Bhartiya supervises. That chain produced a real finding in Mozilla's TaskCluster and carried it from detection through evidence gathering, reproduction, the written report, and follow-up with triage staff. Human review on the receiving end is now the slow part.
Positive - A pipeline of 18 detectors with Claude drafting and Codex independently verifying found and reported a real Mozilla TaskCluster flaw under human supervision. Security research just got faster on the defenders' side.
Negative - The programs receiving these AI-complete reports still triage by hand. The growing review queue, not the finding, is where the exposure now sits.
AgentsAug 26
Marimo patches a flaw that ran MCP commands on notebook open
Marimo patched CVE-2026-75149 today, a code-injection flaw in versions before 0.23.15. Opening a crafted notebook in edit mode could start an attacker-controlled MCP server command as a local subprocess, before a single notebook cell was run; MCP is the protocol that connects AI tools to outside services. Updating removes that path, and it shows how an MCP integration can turn the simple act of opening a file into code execution.
Positive - Marimo patched a flaw that let crafted notebooks launch attacker-controlled local commands before any cell ran.
AgentsAug 26
Vercel's Run SDK isolates code an agent writes
Vercel shipped Run SDK today, which evaluates untrusted JavaScript, or TypeScript with its types stripped out, in a fresh QuickJS context inside a worker thread. Code that runs there cannot reach the surrounding application's systems directly. Agent builders who want a model to execute what it just wrote no longer have to hand it the application itself.
Positive - Vercel’s SDK confines agent-generated JavaScript to an isolated runtime so it cannot directly reach application systems.
TextAug 26
Goodfire opens Silico interpretability platform and a $1 million grant
Silico, Goodfire's platform for examining what happens inside a model when it produces a particular behavior, became generally available today. Goodfire also opened a grant program offering $1 million in free Silico usage to academic and nonprofit interpretability researchers, meaning people who study model internals rather than model outputs. Researchers outside the frontier labs can use it instead of building their own interpretability stack from scratch.
Positive - Goodfire opened its model-inspection platform to the public and funded researcher access, broadening the community able to examine hidden model behavior.
