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OpenCAS

@opencas_ucas

中国科学院大学开源软件与区块链协会
Open Source Software and Blockchain Association of University of Chinese Academy of Sciences
Joined March 2025
292 Following 254 Followers 167 Posts

Latest posts 5

OpenCAS @opencas_ucas
OpenCAS 将以社区支持方身份加入 Metropolis🥳

Metropolis 是一场为期 6 周的黑客松,旨在 Monad 上打造下一代初创项目,并角逐 25 万美元奖金。

来自支持社区的团队还有机会获得额外 5,000 美元赏金。

monad.xyz/metropolis
monad.xyz Metropolis — a Monad hackathon Monad's six-week global online hackathon is your chance to turn an idea into a working product and take it to a global stage.
OpenCAS @opencas_ucas
Interesting work
skysys.eth @skysyseth Jun 4
Sharing our recent work on agentic system security—happy to discuss and collaborate!

Traditionally, Prompt Injection was seen as a one-off attack in a single interaction or context. But in agentic systems, models aren’t stateless assistants anymore—they have memory, filesystems, tools, long-term tasks, and cross-session state. This fundamentally expands the attack surface.

In our paper, we systematically revisit Prompt Injection from a system security perspective and introduce a new attack vector: Cross-Session Stored Prompt Injection. Inspired by stored XSS in web security, malicious prompts can persist across sessions and be re-triggered.

This extends attacks across time (surviving across sessions) and space (via memory, files, tool outputs, retrieved artifacts, etc.), turning a short-lived model-level threat into a long-lived system-level vulnerability embedded in the agent’s execution state.

We formalize stored prompt injection, propose a taxonomy, and provide benchmarks & a sandbox toolkit for evaluating different models, targets, and persistence channels.

Paper: arxiv.org/abs/2606.04425
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OpenCAS @opencas_ucas
在 AI 技术加速融入数字基础设施、区块链应用不断演进的今天,掌握数字资产安全防护方法、学会用 AI 提升学习与工作效率,正在成为每一位技术学习者和行业参与者的重要能力。
5 月 15 日周五19:45 - 21:15,中国科学院大学开源软件与区块链协会将联合 OneKey 举办 OneKey Academy 2026 第三站线上会议
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OpenCAS @opencas_ucas
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skysys.eth @skysyseth Apr 8
The open-source LLM uncensoring tool heretic just hit GitHub Daily Trending #1, which can "crack" Gemma 4 in 90 mins.
We tested our last year's work [1] against it and found something surprising: our method shows strong resilience against heretic. Attackers can hardly strip the model's intrinsic safety mechanisms without catastrophic performance loss.

A new angle for open-source LLM safety governance?

[1] Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction, EMNLP 2025
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