Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Cybersecurity Cybersecurity tasks put long-horizon agent capabilities to the test. In an authorized, isolated environment, Atria Dawn Preview combines environment understanding, vulnerability analysis, tool use, and result verification across the entire task. From analyzing an application and identifying its attack surface to validating vulnerabilities, executing exploits, and applying fixes,...

109,135 Aufrufe • vor 1 Monat •via X (Twitter)

9 Kommentare

Profilbild von The1331Company
The1331Companyvor 1 Monat

Clean workflow

Profilbild von Maria Jose Cadavid G
Maria Jose Cadavid Gvor 1 Monat

up only 📈

Profilbild von neonMustache
neonMustachevor 1 Monat

what toolchain is it using under the hood for static analysis?

Profilbild von SOLANA ID Patris RMA
SOLANA ID Patris RMAvor 1 Monat

How does atria handle zero-days outside known vulnerability sets?

Profilbild von sîlvâ jêrry
sîlvâ jêrryvor 1 Monat

Executing exploits and patching in the same loop is wild

Profilbild von IAM JOKADIVA
IAM JOKADIVAvor 1 Monat

Most agents fail because they can't verify their own output, so having continuous verification across long-horizon vuln fixes is actually a massive shift for devsecops workflows

Profilbild von Andrew Edmond(Ø,G)
Andrew Edmond(Ø,G)vor 1 Monat

continuous verification is the key part here

Profilbild von Pamela AronicoMossMoss | ETHGas ⛽
Pamela AronicoMossMoss | ETHGas ⛽vor 1 Monat

Long-horizon agents in sec are tough. curious how it handles false positives during exploit validation

Profilbild von BuzzHold.eth
BuzzHold.ethvor 1 Monat

Was skeptical about autonomous exploits but seeing verification loop sold me

Ähnliche Videos

a new Chinese AI agent turns research questions into outputs you can check against real evidence. Atria Dawn Preview runs on one idea: a task isn't done because the model says so. it's done when the result runs, checks out, or passes verification. built by Atria ASI I gave it one: build an interactive weather-prediction demo that forecasts the next 6-hour global weather and shows how accurate it is. what it built: a rotating 3D globe with real weather data, a live training loss curve, and the model's prediction sitting next to ground truth across four variables, temperature, wind, geopotential and T850. you scrub through early 2022 and watch prediction vs reality frame by frame. how I checked it: verification is built in. the prediction sits right next to ground truth, and the error map updates as training climbs, so you watch the model actually converge, loss dropping to about 0.003, not just claim it works. one limit: early training steps are visibly noisier, you have to let it run. how it works: > research question to real deliverable, code, docs, tables, even 3D, checked against real evidence > the model reasons, but a separate harness holds the goals, state, permissions and error recovery > its training pipeline uses externally verified task trajectories, including failures, to build reusable capabilities across tasks > 256K context, usable through compatible clients via Anthropic- and OpenAI-style APIs, such as Claude Code or Codex CLI the backstory is the interesting part. around 70% of its R&D team are university students — PhD, master's and undergraduate students — from institutions including Fudan University, Renmin University of China, Peking University and Shanghai Jiao Tong University. young researchers building the tool for doing research. worth a look.

Kirill

28,892 Aufrufe • vor 1 Monat