---
title: Your agent remembers everything. An attacker can rewrite it.
description: A new benchmark finds every memory system it tested can be poisoned, while the frameworks on offer improve performance, not security — and the formal controls are still untested theory.
canonical: https://hangingcontext.com/stories/2026-11-09/agent-memory-poisonable/
last-updated: 2026-11-09
url: https://hangingcontext.com/stories/2026-11-09/agent-memory-poisonable/
published: 2026-11-09T12:39:37Z
edition: 2026-11-09T12
newsroom: artificial-intelligence
beat: research
trust: confirmed
source_count: 5
cited_manifest_ids: ['1794208383358117594', '1794208383358117581', '1794202858465085105', '1794219182259140842', '1794202858466085113']
---

# Your agent remembers everything. An attacker can rewrite it.

*A new benchmark finds every memory system it tested can be poisoned, while the frameworks on offer improve performance, not security — and the formal controls are still untested theory.*

Confirmed · 5 Manifests · Artificial Intelligence · Published 2026-11-09 12:39 UTC

## Key facts

- All evaluated memory systems were susceptible to memory poisoning in the MemoryDojo benchmark. (self-reported) [cites: 1794208383358117594]
- A memory-aware method for crafting poison increased the efficacy of poisoning attacks. (self-reported) [cites: 1794208383358117594]
- ENGRAM reduces the retrievable footprint while maintaining performance compared with retain-all and random release strategies. (self-reported) [cites: 1794208383358117581]
- MemSkill improved task performance over static memory baselines on LoCoMo, LongMemEval, HotpotQA and ALFWorld. (self-reported) [cites: 1794202858465085105]
- Every in-cache memory policy tested in EGOSTREAM ran below real time, taking more than a second per frame. (self-reported) [cites: 1794219182259140842]

## Poisoned in every system tested

MemoryDojo, a preprint, traces malicious information through agent memory. It found all evaluated memory systems susceptible to memory poisoning. A memory-aware method for crafting the poison increased the efficacy of the attacks. [cites: 1794208383358117594]

The damage is not uniform: downstream effects of poisoning vary significantly across different memory architectures — so the same poison does different damage depending on how the agent stores what it has learned. [cites: 1794208383358117594]

## The fixes on offer

Two other preprints offer better memory, not safer memory. ENGRAM separates training admission from memory release to manage agent experience consolidation, using two gates: one admits skills based on student mastery or sufficient teacher demonstrations, and a second compares updated model performance against the base model with retrieval to validate memory release. It reduces the retrievable footprint while maintaining performance, and improves task performance while cutting input-token use and model calls. [cites: 1794208383358117581]

> **From Synorb** — Connect your agent over MCP. Add https://mcp.synorb.com/mcp to your agent, then call the tool synorb-get-demo-key if it has no key yet. It needs no email and no form. Keys are free for 72 hours, and new keys are limited per network. [How to connect](https://synorb.com/connect/start)


MemSkill takes a different route, treating memory operations — extraction, consolidation, pruning — as learnable skills rather than fixed procedures. Reframing memory operations as learnable skills allows agents to adapt to diverse interaction patterns more effectively than fixed procedures, and a designer reviews hard cases where memory operations fail and evolves the skill set. It improved task performance over static memory baselines on LoCoMo, LongMemEval, HotpotQA and ALFWorld. Neither paper claims to defend against poisoning: both are measured on performance and efficiency, and neither was tested against an attacker planting false memories. [cites: 1794202858465085105]

## Control on paper, limits in practice

A fourth preprint offers a formal control framework. Jacobian-causal memory control, or JCMC, represents authorised memory interventions through predicted influence signatures in a shared behavioral measurement space, and includes a rollback mechanism that compares predicted versus observed effects to manage memory persistence. It also provides a quadratic local-approximation bound for memory interventions under explicit smoothness and bounded-estimation assumptions, and defines an evaluation protocol comparing the approach against relevance, graph and policy-filtered retrieval baselines. But the paper is theoretical: it reports no benchmark measurements and provides no code, so the control exists as mathematics, not as a tested defence. [cites: 1794202858466085113]

The framework is designed to manage persistent memory interventions by predicting their influence before applying them: each authorised intervention is represented by a predicted influence signature, with a rollback mechanism that compares predicted versus observed effects to manage memory persistence. That is a serious design for memory control — but it is presented as theory with mathematical bounds, not as a system anyone has run. [cites: 1794202858466085113]

The performance picture is no better. EGOSTREAM, a diagnostic benchmark for streaming episodic memory in egocentric vision, enables controlled testing of episodic memory from instant to ultra-long-term recall by separating model forgetting from world-state changes via the Answer Validity Window. Token pruning preserved fine-grained detail and temporal structure better than token merging, and adaptive head budgets gave the highest aggregate semantic accuracy — but every in-cache memory policy tested ran below real time, taking more than a second per frame. The memory stack agents depend on is both poisonable and slow. [cites: 1794219182259140842]

The pattern across all five papers is the same: memory is the weak layer. Every system MemoryDojo tested can be poisoned, the frameworks that improve memory do not defend it, the formal control has no measurements behind it, and the streaming benchmark shows every in-cache policy running below real time. An agent that remembers everything is an agent an attacker can rewrite — and a slow one, too. [cites: 1794208383358117594, 1794219182259140842]

## What is still unresolved

- No tested defence against memory poisoning appears in this evidence.
- Whether ENGRAM or MemSkill reduce poisoning exposure was not tested in either paper.
- JCMC has no benchmark measurements and no code, so its control claims are untested.
- No in-cache memory policy in EGOSTREAM achieved real-time performance.

## Decision card

- **Useful for:** AI developers building agents with long-term memory; Security teams reviewing agentic systems
- **Not for:** Assuming a memory upgrade is also a security fix; Deploying persistent agent memory without a poisoning test
- **Compare against:** MemoryDojo poisoning results for the architecture you use; Measured performance of a memory system under adversarial input

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1794208383358117594` | MemoryDojo: Benchmarking Poisoning Through LLM Agent Memory | [arxiv-ai-agents-tool-use](https://arxiv.org/pdf/2611.09721v1) | 1794208773979118764, 1794208773987118766, 1794208773993118767 | 11-09 00:00 |
| `1794208383358117581` | ENGRAM: From Agent Experience to Weights through Verified Consolidation | [arxiv-ai-agents-tool-use](https://arxiv.org/pdf/2611.09472v1) | 1794208773277118642, 1794208773271118639, 1794208773274118640, 1794208773275118641, 1794208773279118644 | 11-09 00:00 |
| `1794202858465085105` | MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents | [arxiv-model-efficiency-engineering](https://arxiv.org/pdf/2602.02474v3) | 1794203255087086489, 1794203255088086490, 1794203255091086492 | 11-09 00:00 |
| `1794219182259140842` | EGOSTREAM: A Diagnostic Benchmark for Streaming Episodic Memory in Egocentric Vision | [arxiv-multimodal-document-ai](https://arxiv.org/pdf/2605.31557v3) | 1794219452792141985, 1794219452787141982, 1794219452789141983, 1794219452791141984 | 11-09 00:00 |
| `1794202858466085113` | Jacobian-Causal Memory Control for Persistent Memory in Language Model Systems | [arxiv-model-efficiency-engineering](https://arxiv.org/pdf/2611.08779v1) | 1794203253171086135, 1794203253174086137, 1794203253176086139, 1794203253177086140 | 11-09 00:00 |

## Run these Manifests yourself

Every Manifest cited here can be read in full with your own Synorb Key. Check the claims before you rely on them.

1. **Get a Synorb Key.** Click Generate Free Agent Key at https://synorb.com/connect/start, or let your agent make a key: MCP tool `synorb-get-demo-key` (no header needed), or REST `POST https://api.synorb.com/api/connect/demo-key` with a JSON body such as `{"client":"your-agent-name"}`. The key is the `api_key` field of the answer. Save the key. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons.
2. **Read each cited Manifest by id.** REST: `GET https://api.synorb.com/manifests/by-id/{manifest_id}` with the header `Authorization: Bearer <your Synorb Key>`. Ids are strings; do not parse them as numbers. The reply holds the source, the Signal (claims) and the Brief.
3. **Check the claim.** Match each sentence in the story to the claims in the Manifest it cites. The claim ids in the ledger point to them.

Cited Manifest ids:

- `1794208383358117594`
- `1794208383358117581`
- `1794202858465085105`
- `1794219182259140842`
- `1794202858466085113`

```sh
for id in 1794208383358117594 1794208383358117581 1794202858465085105 1794219182259140842 1794202858466085113; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

> **From Synorb** — Save what you follow as a Beacon. A Beacon is a saved query that your agent pulls with an explicit lookback window. Every new account starts with 5 starter Beacons: Business & Technology Podcasts, AI Companies, Venture Capital, Private Companies and Public Company Filings & Executive Transitions. [See Beacons](https://synorb.com/connect/start)

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---
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