If you're building AI agents with Flowise, Dify, n8n, or similar no-code/low-code platforms, there's a security threat you probably haven't thought about: memory poisoning.
And it's not theoretical. It's in the implements — a lightweight, framework-agnostic scan-before-write pattern.
CODE
from agent_memory_guard import MemoryGuard
guard = MemoryGuard()
result = guard.scan(llm_output)
if result.is_safe:
memory.write(llm_output)
else:
logger.warning(f"ASI06 blocked: {result.threat_type} | score={result.risk_score}")
For Flowise Users
Until Flowise ships a native Memory Guard node, you can add a Function node between your LLM node and your memory store:
CODE
// Flowise Function Node
const { MemoryGuard } = require('agent-memory-guard');
const guard = new MemoryGuard();
const result = await guard.scan($input.text);
if (!result.is_safe) {
throw new Error(`Memory poisoning blocked: ${result.threat_type}`);
}
return $input;
For Dify Users
In Dify, add a Code node between your LLM step and your memory write step:
CODE
# Dify Code Node
from agent_memory_guard import MemoryGuard
import json
guard = MemoryGuard()
result = guard.scan(args["text"])
if not result.is_safe:
raise Exception(f"ASI06 blocked: {result.threat_type}")
return {"text": args["text"]}
This Is Now a Benchmark
The threat model behind this is now formalized as
If you're building no-code agents and want to discuss how to add memory guard validation to your specific platform, drop a comment below.
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