AI agents leak sensitive credentials by storing plain-text secrets in memory, context windows, and execution logs, exposing them to prompt injection, unauthorized privilege escalation, and credential theft.
AI agents require access to sensitive credentials and API keys to perform tasks, but storing secrets directly within agent contexts risks prompt injection, plain-text leaks, credential theft, and unauthorized permission escalation.
Show HN: OneCLI – OSS credential gateway that keeps secrets out of AI agents
Show HN: OneCLI – OSS credential gateway that keeps secrets out of AI agents
if the fake token leaks from my sandbox - it would be completely useless for an attacker
commentNice! I've built something like this, although specific to GitHub: https://github.com/denysvitali/gh-proxy (https://github.com/denysvitali/gh-proxy) The idea being that if the fake token leaks from my sandbox - it would be completely useless for an attacker
Developers and security teams deploying autonomous coding and workflow agents who need to grant network access without exposing raw API keys into agent context or logs.
Repeated complaints focus on agents storing keys in memory/logs and using creative workarounds to bypass boundary permissions.
Unlike standard secret managers (Bitwarden, HashiCorp Vault) that release raw secrets directly to the caller, AgentVault abstracts secrets at the proxy network boundary so the agent never touches or sees the underlying key.
A developer-first secure egress proxy and token-vault that injects real API credentials dynamically into outgoing HTTP requests, giving agents ephemeral scope-limited dummy tokens while keeping real secrets entirely out of the agent context window.
Teams building autonomous agents spend significant engineering hours rolling custom reverse-proxies and risk costly security incidents from prompt-injected key leaks.
“Run autonomous AI agents safely without ever handing them your real API keys.”
A developer-first secure egress proxy and token-vault that injects real API credentials dynamically into outgoing HTTP requests, giving agents ephemeral scope-limited dummy tokens while keeping real secrets entirely out of the agent context window.
Open-source core reverse-proxy CLI/SDK on GitHub, targeting r/LocalLLM, Hacker News, and AI agent frameworks (LangChain, AutoGen, CrewAI).
Inspecting and rewriting egress traffic for multi-step agent tool loops can add latency and slow down execution.
Agents using database drivers, gRPC, or raw TCP sockets may bypass standard HTTP reverse proxies.
Coding agents executing arbitrary python/bash code may attempt network bypasses to fetch environment secrets directly.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 9/10 against 3 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
It sits at the intersection of "ai-powered", "api", "automation", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
