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01/08/2026 10:49 pm
TL;DR: Straiker researcher Carl Vincent’s Andromeda prototype uses two LLM agents to generate position-independent bytecode payloads in real time from a target’s memory context, reducing reliance on static post-exploitation tools and known signatures. The pattern matters because it shows how agentic systems can compress offensive tradecraft into in-memory, context-specific execution that existing detection and review models are not designed to see.
NHIMG editorial — based on content published by Straikerai: Andromeda: One Researcher’s Agentic AI Payload Framework
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: How should security teams govern AI-generated code in production environments?
A: Security teams should treat AI-generated code as normal production code with extra provenance risk.
Q: Why do agentic payloads make traditional detection less effective?
A: Because traditional detection depends on artefacts that are stable enough to recognise later.
Q: What do security teams get wrong about in-memory execution risk?
A: They often focus on hiding files rather than controlling the decision path that creates the payload.
Practitioner guidance
- Restrict runtime code generation paths Identify where AI systems, scripts, or internal tools can generate executable content on the fly, then require explicit approval for those paths in production-like environments.
- Instrument memory-level detection signals Tune monitoring for in-memory execution, dynamic module loading, and unusual function resolution patterns, because static file indicators will miss payloads that are created and discarded inside RAM.
- Separate AI reasoning from execution authority Do not let the same system both decide and directly execute high-risk actions without a control boundary.
What's in the full article
Straiker's full research post covers the operational detail this post intentionally leaves for the source:
- Step-by-step explanation of the Tinkr, Tailor, and Soldir components and how they interact in the agentic workflow
- The live runtime flow for generating and delivering bytecode against a target memory context
- Implementation details on how the prototype uses SKILL.md files and native function documentation to shape payload generation
- The researcher’s discussion of current limitations, including stability, platform coverage, and future experimentation paths
👉 Read Straiker’s analysis of Andromeda and agentic payload generation →
Agentic payload generation: what it means for detection and governance?
Explore further
01/08/2026 10:58 pm
Agentic payload generation creates governance debt faster than most detection stacks can absorb. The important issue is not that a prototype can write code, but that it collapses the distance between decision and execution. When code is produced at runtime from live context, standard review models lose the stable object they were built around. Practitioners should treat this as an emerging policy and telemetry gap, not just an offensive research curiosity.
A question worth separating out:
Q: How can organisations test AI-generated offensive behaviour safely?
A: Use isolated sandboxes that mirror real target conditions closely enough to reveal runtime behaviour, then observe how the agent changes its output under different memory layouts and permission sets. The goal is to learn where the system adapts, where it fails, and which controls stop the transition from lab behaviour to live execution.
👉 Read our full editorial: Andromeda shows how agentic payload generation changes red-team tradecraft
