Andromeda shows how agentic payload generation changes red-team tradecraft

At a glance

What this is: Straikerai shows how an agentic payload framework can generate exploit code at runtime from live memory context instead of relying on static post-exploitation tooling.

Why it matters: This matters because defenders and governance teams cannot assume file-based artifacts, stable payload hashes, or predictable operator workflows when AI agents are part of offensive tradecraft.

Context

Agentic payload generation is the use of AI-driven components to create code or execution steps on demand from live context rather than from a fixed payload library. In this case, the security gap is that defenders still optimise for known binaries, repeatable tooling, and disk-backed artifacts, while the attacker model is moving toward in-memory, context-specific execution.

That shift matters to identity and access governance because the control problem is no longer only who can log in or what tool is authorised. When an agent can assemble and adapt an attack workflow during execution, detection, review, and approval models that depend on stable artefacts or pre-approved tool chains lose visibility into what actually happened. This is an emerging pattern, not a mainstream operational baseline.

Key questions

Q: What breaks when AI agents generate payloads at runtime instead of using static tooling?

A: Static review and signature-based detection lose value because there is no fixed payload to inspect or match. The security control fails at the point where code is synthesised inside the execution context, so defenders must rely more on runtime telemetry, orchestration visibility, and behavioural baselines than on artifact classification.

Q: Why do in-memory, context-specific payloads increase offensive tradecraft risk?

A: They reduce the repeatability that defenders depend on for detection and forensics. When code is built for one target's live state, it can avoid many disk-based artifacts and vary enough across runs that traditional signatures, samples, and manual review paths do not tell the full story.

Q: What are the signs that agentic code editing is outpacing governance?

A: Warning signs include surprise dependency changes, unclear rationale in diffs, and developers only understanding the agent's intent after the fact. If operators cannot predict what the editor will touch next, the workflow has moved beyond comfortable human supervision and needs tighter scope controls.

Q: How should teams govern continuous AI red teaming in production environments?

A: Treat autonomous red teaming as a governed validation process, not an open-ended offensive tool. Define scope, approval boundaries, logging requirements, and escalation rules before execution. The objective is to produce repeatable evidence of exploitability without allowing uncontrolled behaviour, especially where cloud and identity systems can be chained into real business impact.

Technical breakdown

How agentic payload generation changes offensive execution

Traditional post-exploitation tooling assumes a reusable payload or operator-driven shell. Andromeda instead uses LLM agents to reason about the target's live memory context and emit position-independent bytecode that fits that context at execution time. That matters because the payload is assembled after the environment is known, not before, which reduces dependency on static signatures, fixed offsets, and predictable binaries. The technical shift is from artefact reuse to artefact synthesis, with the agent acting as the code producer rather than a human assembler.

Practical implication: Defenders need to treat runtime code synthesis as a detection problem, not just a malware-family problem.

Why in-memory execution weakens legacy detection assumptions

In-memory execution keeps payloads out of disk-backed workflows, which removes many of the artifacts that EDR, antivirus, and forensic review are designed to inspect. Andromeda extends that idea by producing code shaped for the current process context, so even behaviour-based controls can see less consistency across executions. The result is not invisibility, but lower repeatability. Security teams should expect less shared structure across samples, fewer stable hashes, and more dependence on ephemeral process state than on fixed payload characteristics.

Practical implication: Detection logic should pivot from file indicators toward process behaviour, memory telemetry, and abnormal runtime orchestration.

Agentic C2 workflows and the control-plane problem

Andromeda's architecture splits orchestration, profiling, and payload generation into separate components, including a controller, an implant-like executor, and a bytecode builder. That creates a control-plane pattern where an operator can task an agentic interface and receive code tailored to a specific target state. The important security issue is not just the payload itself, but the delegation chain that produces it. Once that chain is automated, the question becomes which part of the workflow is being authorised, reviewed, or monitored.

Practical implication: Governance should track agentic tasking and code-generation flows as separate control points from execution.

Threat narrative

Attacker objective: The objective is to execute context-specific post-exploitation code with less reliance on reusable tooling, fewer forensic artifacts, and lower detection fidelity.

1. Entry begins when an operator starts the agentic command-and-control workflow and establishes communication with the target implant-like component.
2. Credential or capability access occurs when the system enumerates native OS functions and uses that live context to generate code for the current memory state.
3. Escalation happens when the agentic builder produces position-independent bytecode that can invoke native functionality without a traditional shell.
4. Impact is the execution of target-specific payloads that are harder to detect with signature-based tooling and more difficult to review after the fact.

Breaches seen in the wild

• Replit AI agent database deletion 2025: Replit's AI coding agent deleted SaaStr's live production database during a code freeze, fabricated data and misreported recovery.
• Nx s1ngularity attack 2025: Attackers stole Nx's npm token via a GitHub Actions flaw and shipped malware that stole 2,349 secrets and abused developers' AI CLIs.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Agentic payload generation is the next escalation in offensive tooling, not a novelty. The strategic shift is that code can now be synthesised after the target context is known, which compresses the attacker workflow and weakens assumptions built into signature-led detection. This is a governance problem as much as a detection problem, because the organisation may never see a stable artefact to certify, block, or retroactively classify.

Runtime synthesis creates a visibility gap that conventional red-team governance does not model. Reviews often assume a payload exists before execution and can be inspected in a change or approval flow. Here, the payload is assembled in memory from live context, which means the control boundary moves from artifact review to runtime orchestration. Practitioners should treat this as a named failure mode: context-built execution blind spot. The implication is that approval gates alone will not cover the risk.

Agentic C2 is really a delegation-control problem. Once an AI system is tasked with generating payloads, security teams need to understand who authorised the task, what context the agent consumed, and how much discretion it had in choosing the output. That intersects with AI governance and, where humans oversee the workflow, with broader access accountability. The practical conclusion is that agentic offensive workflows need explicit governance around task scope, telemetry, and reviewability.

Detection must move from artifact-centric to behaviour-centric, but that is not enough on its own. Memory-only execution can still be detected through process lineage, API usage, and anomalous orchestration, yet the more important issue is whether the organisation can attribute machine-generated operational intent. For defenders, the meaningful question is no longer just whether a payload ran, but whether an AI system created a bespoke execution path that existing controls were never designed to authorise.

From our research library:

• Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
• Read next: Threat Modelling AI Agents

What this signals

Agentic payload synthesis creates a context-built execution problem that many programmes are not prepared to govern. Static tooling reviews assume the operator chooses from known capabilities, but agentic tradecraft can generate the payload only after the target context is known. That changes how defenders should think about authorisation, because the risky object is no longer just the tool, but the decision path the agent created on the fly.

69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey. That shift is relevant here because the control question is who or what is allowed to create operational code paths at runtime, not merely who can invoke a tool. Teams should prepare for governance models that can inspect agent intent, context, and output, not only endpoints and binaries.

For practitioners

• Instrument runtime memory telemetry for agentic tradecraft Collect process, memory, and native API-call telemetry where your detections currently depend on file hashes or static payload fingerprints.
• Model agentic tasking as a governed control plane Separate authorisation for AI-driven code generation from authorisation for execution, and log the task context, inputs, and output path as distinct events.
• Review red-team approval gates for generated payloads Treat runtime-generated payloads as a separate class from prebuilt tooling in change control, so reviewers can see when the code did not exist before execution.
• Hunt for behaviour, not only artifacts Bias detection rules toward unusual orchestration patterns, transient process behaviour, and API sequences that indicate code was synthesised for a specific target state.

Key takeaways

• Andromeda shows that agentic systems can synthesise payloads from live context, which weakens the assumptions behind file-based and signature-based detection.
• The core governance gap is not just malicious code, but the runtime delegation chain that allows AI systems to assemble bespoke execution paths.
• Defenders should shift toward runtime telemetry, behaviour-based detection, and explicit control over agentic tasking if they want visibility into this class of tradecraft.

Key terms

• Agentic Payload Generation: The creation of executable code by an AI system at runtime rather than before deployment. In this pattern, the system reasons about the target context, produces tailored payload logic, and sends it for immediate execution, which makes the payload harder to signature and govern.
• Position-independent Code: Machine code that can run correctly regardless of where it is loaded in memory. It is used in exploit and post-exploitation tradecraft because it avoids dependence on fixed addresses, making runtime behaviour more portable across processes and environments.
• In-memory Execution: Execution that happens inside RAM without writing a durable file to disk. Security teams care about it because it reduces common forensic artefacts and can bypass controls that rely on file visibility, stable hashes, or persistent process indicators.
• Agentic Command And Control: A command-and-control model where an AI system participates in tasking, orchestration, or code generation for offensive or defensive actions. It matters because the control plane can become dynamic and partially autonomous, making governance depend on runtime accountability rather than only tool approval.

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