send_email
PulseAugur coverage of send_email — every cluster mentioning send_email across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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AI agents struggle with tool selection in large menus, study finds
An experiment revealed that AI agents struggle with tool selection when presented with a large number of options. While raw accuracy dropped significantly as the number of tools increased from 5 to 50, a chance-correcte…
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reskSecure introduces LLM firewall to prevent PII exfiltration
A new security tool called reskSecure has been introduced to protect customer-support AI assistants from sensitive data exfiltration. The tool acts as an LLM firewall, blocking forbidden phrases and unauthorized tool ca…
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AI agents can avoid duplicate emails using idempotency keys
This article discusses a common challenge in AI agent development: preventing duplicate email sends when network connections fail. The author, a founder of Hail, explains that timeouts do not necessarily mean an email f…
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AI agent security: shift focus from prompts to tool permissions
This cluster of articles discusses AI agent security, emphasizing that the primary vulnerability lies not in prompt manipulation but in the broad permissions granted to agents. The core issue is that agents, which combi…
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New 200-line tool verifies AI agent actions, not just output
A new Python tool called agentverify, approximately 200 lines of code, has been developed to detect when AI agents exfiltrate data through their tools, a vulnerability that standard output-based testing often misses. Th…
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New LLM-RedKit tool targets agentic prompt injection vulnerabilities
A new open-source tool called LLM-RedKit has been developed to address the evolving threat of prompt injection attacks against AI agents. Unlike previous tools focused on static prompts, LLM-RedKit specifically tests ag…
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AI agents need sophisticated error handling beyond simple retries
Building a robust AI agent requires more than just a simple retry mechanism for failures. The agent must be able to distinguish between different types of errors, such as transient network issues, validation problems wi…
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AI agents pose security risks when given tool access; mitigation strategies outlined
Giving AI agents access to tools like function calling, code interpreters, or APIs introduces significant security risks, as models cannot reliably distinguish between instructions and data. This means a prompt injectio…
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LLM agents need structured email contracts for better debugging and operations
This article discusses the importance of defining clear contracts for email actions performed by LLM agents, rather than solely focusing on the prompt. It argues that a well-defined, versioned email contract, including …
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MCP Trust Pack adds security layer for AI agent tool calls
The MCP Trust Pack is a new security layer designed to enhance the safety of tool calls made by AI agents within the MCP framework. It introduces a four-layer evaluation process—vetting, policy, consent, and taint—to de…