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[ARTICLE · art-152018] src=adikshatriyag-crypto.github.io ↗ pub= topic=ai-safety verified=true sentiment=· neutral

Show HN: AegisKernel – Test prompt injection attacks on AI agents in the browser

A developer released AegisKernel, a browser-based sandbox for testing prompt injection attacks against AI agents, claiming its deterministic capability boundaries block 100% of autonomous tool hijacking in sub-millisecond interception times. The tool includes a red-team attack vector generator and a two-line Python integration for LangChain, CrewAI and AutoGen, in which an AegisShield object wraps an agent tool via protect_tool under a strict SecurityPolicy. The release argues probabilistic LLM guardrails fail against tool hijacking, positioning deterministic execution interception as the defense.

read1 min views24 publishedOct 17, 2026

Live Threat Interception Sandbox #

Experience why probabilistic LLM guardrails fail and how deterministic capability boundaries block 100% of autonomous tool hijacking in sub-millisecond time.

Attack Vector Generator Red Team

AegisKernel Protection:

Execution Interceptor Telemetry

          STANDBY
// AegisKernel Zero-Trust Interceptor Ready

// Awaiting agent execution event...

Try running one of the attack vectors on the left.

INTERCEPTION TIME

-- ms DEFENSE VERDICT

IDLE

FORENSIC HASH

--

### 2-Line Zero-Trust Integration

LangChain / CrewAI / AutoGen Ready

from aegis import AegisShield, SecurityPolicy
shield = AegisShield(policy=SecurityPolicy.strict())

safe_tool = shield.protect_tool(vulnerable_agent_tool) # 🛡️ 100% Deterministic Immunity

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