As I share more personal information with MUSE, I want to understand the environment I’m trusting. When I ask it to work with my information, what software is available to it? How well is that software maintained? What could the security of that environment mean for my data?
I started with something I could inspect: the software inventory of the environment MUSE could access in my session. I asked it to generate a software bill of materials, or SBOM, for its VM, then used that inventory for a vulnerability review in Interlynk.
The inventory identifies Ubuntu 24.04.5 LTS and a collection of operating-system packages, developer tools, and language libraries. The vulnerability export contains 279 finding rows, including 14 rated Critical and 115 rated High. Those results give me specific questions to ask about maintenance and exposure. They do not, by themselves, establish whether my personal information is safe or whether it has been compromised.
You can explore the findings yourself: View the MUSE VM vulnerability report in Interlynk using this ShareLynk.
What environment did I actually inspect?
This is a view of the environment exposed to MUSE’s tools in one session. The SBOM identifies installed software, including Ubuntu, Python libraries, Go dependencies, and utilities such as curl and rsync. It does not identify the physical host or hypervisor, establish how sessions are isolated, or show where the model itself runs.
Meta provides architectural context in How We Built Safety Into Muse. Those design claims complement this inventory; the scan does not independently verify that the protections operate as described.
Meta describes a dedicated VM for each user, with the agent and its tools inside an isolated runtime container. Credentials and security services sit outside that container, while Sentinel governs connector actions and outbound network requests. These boundaries matter when assessing the impact of a vulnerable runtime component. The article also explains that Meta can access data for service operations and that sanitized interaction data may be used for training unless users opt out. It presented Confidential VMs as a forthcoming capability at publication.
My interpretation is that the SBOM likely covers the agent-accessible runtime rather than the entire VM security architecture. Its coverage of the host and protective services remains unverified. The Ubuntu inventory also warrants clarification against the Debian runtime image described in Meta’s article.
The question this review adds is straightforward: what software does the agent execute, and how are reported vulnerabilities in that software addressed? A component finding does not demonstrate a failure of isolation or exposure of personal data.
What I can examine is whether software present in this environment matches known vulnerability records. The critical findings cover networking, file synchronization, image processing, fonts, and a shell package. If affected functionality processes sensitive data or untrusted input, those findings could matter directly to the confidentiality and integrity of my work.
The vulnerability figures in this post come from Muse-2026-09-24-Vulnerabilities-Filtered-All.csv, the authoritative export for this review. It relates to the September 24 inventory; its Updated and Assigned timestamps are September 25, 2026. Every row identifies OSV as its source. The earlier local Trivy report is a separate snapshot and is not the basis for these totals.
What the report contains
Severity in the export | Finding rows |
|---|---|
Critical | 14 |
High | 115 |
Medium | 105 |
Low | 35 |
Not specified (blank) | 10 |
Total | 279 |
The export contains 225 distinct vulnerability IDs across 50 component-name/version combinations. Grouping by vulnerability ID, component name, and component version leaves 230 distinct combinations. The CSV does not include installation paths or component identifiers that would explain every repeated row, so the headline preserves the exported row count.
The 14 critical rows represent 12 distinct vulnerability IDs across seven components. The fontTools and Pillow critical findings each appear twice with the same component name and version. That is why “14 critical findings” and “12 distinct critical vulnerabilities” are both useful, but different, descriptions of this export.
The critical findings
From a user’s perspective, the consequences matter more than the CVE count. A flaw in certificate validation could weaken confidence in the destination of a connection. An image-processing flaw could expose memory from a worker handling files. A command-injection flaw could let attacker-controlled input affect what a process executes. The advisories below describe those kinds of failures, but whether they can occur in this MUSE session depends on configuration, patch status, and which code paths are used.
The table below includes every distinct critical ID. Scores and installed versions are reproduced from the CSV. For readability, Ubuntu advisory IDs are shown without their UBUNTU- prefix; for example, UBUNTU-CVE-2026-53793 appears as CVE-2026-53793.
Component | Installed version | Critical vulnerability IDs and reported CVSS scores | Export rows |
|---|---|---|---|
rsync | 3.2.7-1ubuntu1.5 | CVE-2026-53790 (9.2), CVE-2026-53791 (9.1), CVE-2026-53793 (9.1), CVE-2026-70452 (9.1), CVE-2026-70460 (9.2) | 5 |
curl | 8.5.0-2ubuntu10.13 | CVE-2026-19931 (9.8), CVE-2026-18924 (9.1) | 2 |
fontTools | 4.46.0 | CVE-2025-66034 (9.8) | 2 |
Pillow | 10.2.0 | CVE-2026-54058 (9.1) | 2 |
GraphicsMagick | 1.4+really1.3.46-2 | CVE-2025-32460 (9.1) | 1 |
dash | 0.5.12-6ubuntu5 | CVE-2026-91187 (9.3) | 1 |
AnyIO | 4.12.1 | CVE-2026-63374 (9.3) | 1 |
rsync: the largest concentration of critical findings
The report flags five distinct critical vulnerabilities in rsync, alongside 19 high findings. Several concern the boundaries that determine which files or services a remote party can access:
CVE-2026-53790: command and argument injection through affected rsync execution paths can allow attacker-controlled input to execute commands with the process’s privileges. Ubuntu advisory.
CVE-2026-53791: a forged PROXY protocol source address can bypass IP-based daemon access rules under the affected conditions. Ubuntu advisory.
CVE-2026-53793: affected handling of an inner module root can allow file access outside the intended subtree. Ubuntu advisory.
CVE-2026-70452: failed DNS lookups can cause hostname-based deny rules to be skipped. Ubuntu advisory.
The fifth critical entry is CVE-2026-70460, scored 9.2 in the export. Ubuntu’s individual advisory currently provides no detailed description and marks Ubuntu 24.04 as Needs evaluation. The other four rsync advisories also list that distribution status. These are priorities for checking the installed build and actual configuration; their presence in the inventory does not establish that an exposed rsync daemon is running. Ubuntu CVE-2026-70460 advisory.
curl: authentication and connection handling
The two curl findings affect different libcurl behaviors. CVE-2026-19931, scored 9.8 in the export, involves reusing a connection authenticated for one user for another user’s request under particular Negotiate authentication conditions. CVE-2026-18924, scored 9.1, concerns a use-after-free during cleanup of HTTP/2 Server Push streams when connections are shared between handles. Authentication advisory, HTTP/2 advisory.
For this VM, the useful follow-up is to identify applications using libcurl and determine whether they exercise those features. The package name alone does not tell us which connection options an application enables.
AnyIO: TLS validation for internationalized domain names
CVE-2026-63374 affects AnyIO’s handling of non-ASCII hostnames in connection paths including connect_tcp() and TLSStream.wrap(). Different hostname encodings can let a certificate for a different destination validate when an attacker has hijacked or redirected the connection. The CSV records AnyIO 4.12.1 and a score of 9.3; the maintainer lists 4.14.2 as patched. The exposure depends on the application’s use of the affected hostname and connection paths. AnyIO advisory.
Pillow and fontTools: risks in processing untrusted files
The Pillow entry, CVE-2026-54058, concerns a crafted McIdas AREA image opened through a memory-mapped file path. Subsequent pixel operations can read beyond the mapped region, exposing process memory or crashing a worker. That makes image upload, preview, and conversion workflows relevant places to investigate. The CSV records Pillow 10.2.0; the fix is in 12.3.0. Pillow advisory.
For fontTools 4.46.0, CVE-2025-66034 concerns processing a malicious .designspace file through the affected fontTools.varLib entry point. It can permit arbitrary file writes and lead to code execution. The maintainer lists 4.60.2 as patched. fontTools advisory.
Both vulnerabilities are Critical in this export. Their maintainer assessments differ, which reinforces the need to retain the source and scoring context when discussing severity.
GraphicsMagick and dash: findings that need further evaluation
CVE-2025-32460 concerns a heap buffer over-read in GraphicsMagick’s JPEG XL image reader. The export rates it Critical at 9.1, while Ubuntu’s advisory displays a different score and marks Ubuntu 24.04 as Needs evaluation. Confirming the installed build’s patch status and relevant image-processing support is necessary before declaring this VM exposed. GraphicsMagick advisory.
The export also flags CVE-2026-91187 against dash 0.5.12-6ubuntu5, at 9.3. Ubuntu’s record has only a brief cryptographic-signature-verification description and also says Needs evaluation. That is insufficient detail to claim a confirmed shell exploit on MUSE; the package mapping and applicability need investigation. Ubuntu dash advisory.
What would help me judge whether my information is protected?
The scan gives me a starting point for a more concrete conversation with the provider:
Data access: Which personal information, uploaded files, and credentials can this runtime access? What is stored elsewhere?
Isolation: How are users and sessions separated, and what permissions and network access does the runtime have?
Exposure: Which flagged components are actually used, and can untrusted files or network responses reach their affected functions?
Maintenance: Which findings are applicable, which are already patched or mitigated, and how quickly are runtime images updated?
Retention: What remains after a session ends, including temporary files, logs, and backups, and how is it deleted?
Meta’s article supplies design context for some of these questions. The SBOM and CSV add component names, versions, and advisory IDs that make maintenance and exposure questions specific enough to investigate. Neither the inventory nor this review verifies the implementation of the broader security controls.
Every row in the CSV has a status of Unspecified. It also lacks fixed-version and exploitability-assessment fields. The report therefore identifies work to investigate; it does not establish that these findings have been triaged or remediated, and it cannot support a count of available fixes.
Start with the critical findings, identify the applications and features involved, and record whether each issue is applicable. For Ubuntu packages, check the distribution’s package revision and security status. For Python libraries, identify the environment that imports them and validate updates there. In a provider-managed runtime, bring the relevant component versions and advisory IDs to the provider, then review a fresh inventory after changes.
How to inspect your own MUSE environment
You can begin with a focused request to MUSE:
Inventory the packages, libraries, applications, and operating system in this VM, excluding individual file components. Use Trivy to produce a CycloneDX 1.7 JSON SBOM and provide a separate vulnerability report. Give me both files to download.
My first Syft inventory included more than 60,000 individual file components. Narrowing the inventory to software packages made it easier to review. For this walkthrough, the upload artifact is vm-trivy-2026-09-24.cyclonedx.json, generated using Trivy’s conversion of its JSON report. The vulnerability analysis in this post uses the Interlynk CSV export described above.
Interlynk’s free tier provides centralized SBOM storage, quality checks, vulnerability scanning, and editing.
If your MUSE session has browser access and file-upload capabilities, you can ask it to help with the next steps too:
Help me sign up for Interlynk’s free tier at https://app.interlynk.io/register. Let me complete any authentication steps that require me. Once I’m signed in, create a product called “MUSE VM” and upload the Trivy-generated CycloneDX 1.7 SBOM. Confirm that the upload succeeded and give me a link to review the findings. If I already have an account, help me sign in and use it instead.
You may need to choose a signup method and complete sign-in or email verification yourself. MUSE can then continue with the product creation and upload where its available tools support those actions.
The file to upload in this example is vm-trivy-2026-09-24.cyclonedx.json. Interlynk supports CycloneDX 1.7 JSON. The raw Trivy JSON report and exported vulnerability CSV serve different purposes from the SBOM import. If you prefer to upload it yourself, create the product under Products, open it, select Upload SBOM, choose an environment, and select the file. See the product guide and upload guide.
Once the upload is processed, review critical and high findings, check installed versions and advisory conditions, and document the outcome. Keep repeated component findings and distinct vulnerabilities separate when reporting totals. When the VM changes, ask MUSE to generate a fresh SBOM and upload it under the same product so you can follow up on affected components.
I now have a clearer picture of the software in the environment exposed to my MUSE session, and a list of reported vulnerabilities to investigate. I still need evidence about isolation, data access, retention, and the applicability of these findings to assess how well my personal information is protected. Keeping the SBOM in Interlynk gives me a way to revisit the software findings as the environment changes and the provider addresses them.