reshabh

4 ms·

1.
PAuth – Precise Task-Scoped Authorization for Agents(arxiv.org)
2 points by reshabh 5mo ago|0 comments
2.
Tool-space interference in the MCP era(microsoft.com)
3 points by reshabh 1y ago|0 comments
3.
AI Models Need a Virtual Machine(blog.sigplan.org)
26 points by reshabh 1y ago|1 comments
4.
Agentic Workflows: Towards Natural‑Language Programming for GitHub Actions(githubnext.com)
3 points by reshabh 1y ago|0 comments
5.
GitHub Models CLI now auto-generates test cases and an evaluator(github.blog)
1 points by reshabh 1y ago|0 comments
6.
Testing AI Software Isn't Like Testing Plain Old Software(blog.sigplan.org)
2 points by reshabh 2y ago|0 comments
7.
PromptPex: Automatic Test Generation for Language Model Prompts(arxiv.org)
2 points by reshabh 2y ago|1 comments
8.
by reshabh 2y ago
PromptPex is an LLM-based tool to automatically generate and evaluate unit tests for a given AI model prompt. PromptPex extracts input and output specifications from the prompt and uses them to generate diverse, targeted, and valid unit tes
9.
How to Hack AI Agents and Applications(josephthacker.com)
2 points by reshabh 2y ago|0 comments
10.
2025 Top Risk and Mitigations for LLMs and Gen AI Apps(genai.owasp.org)
2 points by reshabh 2y ago|0 comments
11.
Software Security Analysis in 2030 and Beyond: A Research Roadmap(arxiv.org)
1 points by reshabh 2y ago|0 comments
12.
by reshabh 3y ago
For prompt injection attacks which are context-sensitive, we have developed a DSL (SPML) for capturing the context and then we use the same to detect conflict with the originally defined system bot / chat bot specification. Having rest
13.
by reshabh 3y ago
SPML, a meta language designed for writing system prompts, includes high-level language features such as support for user-defined types and comments. These features make system prompts easier to develop and more maintainable compared to tho
14.
by reshabh 3y ago
Prompt injection attacks represent a significant challenge for LLM-based systems, such as chatbots. Several techniques are in place to proactively detect these attacks, including classifying the input prompt as either safe or unsafe, or det
15.
SPML: A DSL for Defending LLMs Against Prompt Attacks(arxiv.org)
6 points by reshabh 3y ago|2 comments
16.
GWP-ASan: Sampling-Based Detection of Memory-Safety Bugs in Production(arxiv.org)
2 points by reshabh 3y ago|0 comments