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Persistent memory is what makes an AI agent useful across sessions, and it also gives a bad instruction a way to outlive the conversation that introduced it. OpenClaw is a concrete case. Its memory is a set of workspace files that are written, indexed and later retrieved, so whatever gets written can shape what the agent does in a later session. The security question shifts from “what did the model read?” to “what was allowed to be written, and what gets recalled later?”
Yes, prompt injection can persist across conversations when an injected instruction or a false fact is saved to memory and later recalled. OpenClaw’s design tries to keep untrusted content out of curated memory and out of automatic recall. That is the project’s design statement. External analysis describes the risk pattern, and experimental work reports attack results under its own test conditions. Each of those is a different kind of evidence, and this article keeps them separate.
Why does my AI agent forget everything between sessions?
Without a memory layer, a model starts each session from its prompt alone. OpenClaw states the principle plainly in the design-principles section of its Memory architecture page: “No hidden state. The model only remembers what is written to files in the agent workspace.” Any continuity a user experiences comes from the system deciding what to save and what to bring back into a later session.
That makes forgetting the default and remembering a deliberate pipeline with two steps, writing and retrieval. Each step is where the system becomes useful, and each is also where something can go wrong.
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How OpenClaw memory is organized
OpenClaw’s Memory overview describes three kinds of Markdown file in the agent workspace. The default Memory Core adds a SQLite index on top. The Memory architecture page says these tiers carry distinct trust levels, write rules and injection behavior, so each one should be read differently.
USER.md
This file holds stable preferences and active context. Because it describes the user, a wrong or planted line here reads as a fact about you rather than a passing note. It is the tier where a bad entry is most likely to be taken as settled.
MEMORY.md
This file holds long-term facts and decisions. Entries are meant to last, so a decision recorded here can keep shaping later work long after the conversation that produced it.
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Dated notes
These hold observations and running context. Their date stamps are the easiest way to see when something entered memory, which matters when you are tracing where a bad entry came from.
SQLite index
The index is built over the stored memory and supports retrieval. Whether removing a file also clears its index entry is one of the first questions to answer for any deployment, and it is covered in the deletion steps below.
Why memory changes the security problem
Ordinary prompt injection is a current-session problem. A hostile instruction in a web page or document can steer the agent while it reads that content, and the effect ends when the session does. Persistent memory changes the timing. If the agent writes the injected text, or a misleading conclusion drawn from it, into memory, the influence is carried into sessions that never encountered the original source.
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Google’s security analysis of OpenClaw, published as “OpenClaw in the Wild: Security Analysis of Autonomous Agents,” treats this as part of a larger pattern rather than a standalone bug. In its framing, indirect prompt injection, memory poisoning, unsafe tool use, data exfiltration and malicious skill abuse are stages at which untrusted influence moves into contexts with more privilege. Memory poisoning is where that influence gains persistence. The analysis describes a risk pattern in the system; it does not claim a confirmed exploit in every category.
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Only if the system decides what is allowed into memory and records where each entry came from. That is the core tension: a memory that stores useful context will also store whatever content gets labeled as context. OpenClaw places its main control at write time, before anything is recalled. Its architecture page says poor selection when content is written can degrade memory even when retrieval works well, and it calls the write path “the security boundary.”
Origin labels travel with each memory
OpenClaw sorts content into four origin labels: owner, agent-derived, untrusted and system. The labels are stored as metadata rather than inferred from the wording of a memory. A line that says “the owner approved this” cannot promote itself, because the label records where the content came from regardless of what the sentence claims.
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Untrusted content is kept out of curated memory
According to the documentation, content with an untrusted origin is kept out of curated core memory and out of ordinary automatic injection. Consolidation, the background curation step, runs provenance checks, so a memory’s origin can be traced independently of its prose. The architecture page also describes session-kind restrictions on what can be promoted into memory. These are design choices to evaluate, not a guarantee that the risk is gone.
Can prompt injection persist across conversations? What the evidence shows
A September 2026 arXiv preprint, “When Malicious Instructions Persist: Persistent Memory Poisoning Attack on Harness-Based Agents,” reports experiments on persistent memory poisoning. Its reported average injection success rates were:
| System (as named in the paper) | Average injection success rate |
|---|---|
| OpenClaw | 73.7% |
| Claude Code | 66.9% |
The paper also reports cross-session attack success rates of 55.5% and 81.7%. Check these against the paper’s own per-system breakdown rather than assuming which figure belongs to which system.
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These are outcomes under the paper’s tested settings, configurations and attack designs. They describe what a crafted attack achieved in that experiment, not how often agents in the wild encounter or succumb to such attacks. Published material on this topic does not include a population-level count of real-world OpenClaw memory-poisoning incidents, so these percentages cannot be converted into an incident rate. A preprint is also posted before formal peer review, so treat its figures as the report of one experiment rather than a settled benchmark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can I delete what my AI agent remembers?
You can remove what you can find, but OpenClaw’s documentation is explicit that deletion controls do not cover every workspace write or retained copy. The “Memory provenance and deletion” documentation is the place to confirm the current behavior for your version. Work through the workspace in this order:
- Read USER.md, MEMORY.md and the dated notes, and list every entry you do not want the agent to recall.
- Remove or correct those entries in the Markdown files. Expected result: the text no longer appears in the file.
- Check the SQLite index for the same content. If your setup does not clear index entries when a file changes, the entry remains and needs separate handling.
- Search the workspace for other copies of the text, including writes made by tools and any logs or exports the agent produces.
- Check backups and synced folders that contain the workspace, because retained copies can fall outside deletion controls.
- Start a new session and ask about the removed item. If the agent still recalls it, the copy you missed is the likely source.
Where the controls stop
- Taint coverage is incomplete. Only tools that declare their results as network-sourced take part in tainting. The documentation’s own example is local file output, which may not be treated as untrusted, so content a local tool writes can reach memory without that label.
- Deletion does not reach everything. The project says its deletion and exclusion controls do not cover every workspace write or retained copy.
- Shared agents widen who can steer. OpenClaw’s Security Policy notes that when several people can message a tool-enabled agent, each one can steer it within the permissions that agent holds.
- Sandboxing is off by default. The “Why OpenClaw” documentation says so, and it warns that its architecture comparisons are not security certifications. Running OpenClaw on your own machine is not, by itself, isolation.
How to evaluate any agent memory system
These six dimensions are useful decision axes. They are not a ranking of memory architectures, so use them to ask the same questions of any system:
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Quick Recap
- Write-time curation: what can be saved automatically, and what needs confirmation from a user or operator?
- Provenance: can a memory’s source and session be traced without relying on its wording?
- Recall behavior: what is injected automatically, what needs an explicit search, and how much can come back?
- Review and correction: can people inspect, edit, supersede or remove stored facts?
- Deletion coverage: do deletions reach indexes, derived summaries, backups and copies?
- Privilege and isolation: what tools and accounts can the agent use, and does execution run in a sandbox?
What remains unverified
- Whether OpenClaw’s memory gates hold up across real deployments. No independent audit of their effectiveness is available in the published material on this topic.
- Whether memory poisoning is unique to OpenClaw. The evidence here covers OpenClaw and one comparison harness. It does not show that other memory systems are safer or equally exposed.
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