TL;DR: AI development containers can still leak binaries, environment variables, and other secrets when file download endpoints allow path traversal into /proc, according to Equixly’s analysis. Container isolation reduces risk, but it does not replace input validation, file access allowlisting, or secrets governance for LLM workloads.
At a glance
What this is: Equixly shows that AI containers remain vulnerable when file download endpoints can traverse into /proc and expose binaries, environment variables and other sensitive runtime data.
Why it matters: IAM, PAM and NHI teams should care because the weakness turns container runtime access into credential exposure and secrets governance failure for LLM workloads.
Context
AI development containers are often treated as a security boundary, but this article shows that the boundary collapses when file retrieval endpoints accept attacker-controlled paths. In practice, the problem is not containerisation itself but the combination of path traversal, /proc access and weak file allowlisting around runtime data.
For LLM and agentic workloads, the identity angle is real: environment variables, API keys, tokens and other secrets often sit inside the process context rather than in a dedicated secrets boundary. That makes NHI governance, secrets handling and file access controls part of the same control surface, not separate problems.
The article's case is typical of a wider pattern in AI application security. Teams assume the container protects them, then discover that runtime introspection and file system shortcuts give attackers a direct path to sensitive material.
Key questions
Q: What breaks when AI container download endpoints allow path traversal into /proc?
A: The application loses the separation between user-controlled file access and process state. That allows attackers to read environment variables, open file descriptors and binaries that were never meant to be downloadable. In practice, the failure is not container escape but uncontrolled read access to runtime secrets and internals.
Q: Why do /proc exposures create a credential risk for AI workloads?
A: AI containers often keep API keys, database strings and signing material in environment variables or reachable process resources. If an attacker can read /proc/self/environ or enumerate file descriptors, those secrets can be recovered without cracking the container itself. That turns a file access flaw into a secrets exposure problem.
Q: How can security teams tell whether their container isolation is still adequate?
A: They should test whether the workloads they place in containers would be acceptable if the kernel boundary collapsed. If a kernel escape would expose sensitive data, privileged services, or neighbouring tenants, then the current isolation model is too weak for that workload. Adequacy is measured by blast radius, not by whether a container exists.
Q: Should organisations treat AI container file access and secrets management as one control area?
A: Yes. When file traversal can reach process state, file access and secrets governance become the same attack surface. Organisations should review whether application file handling, runtime secret storage and NHI credential rotation are governed together, because a weakness in one can expose the others.
Technical breakdown
Why /proc turns container files into a runtime exposure path
Linux /proc is a virtual file system that exposes process metadata and live handles, including open file descriptors and environment data. If an application lets a user supply a file path, an attacker can pivot from ordinary file download requests into /proc/[PID]/fd, /proc/[PID]/exe or /proc/[PID]/environ. In that situation, container boundaries do not matter much because the attack targets the process view inside the namespace rather than the intended application file store. The real issue is that the app has given the requester a read primitive over runtime state.
Practical implication: Treat any download or preview endpoint as a process access surface, not just a file access feature.
How path traversal defeats file allowlisting
Path traversal works when the application accepts relative path segments such as ../ and resolves them before access checks are applied, or when it checks the wrong path representation. In AI platforms, this often starts with a seemingly narrow retrieve function and ends with direct reads from unexpected system paths. A file allowlist is only effective if resolution, normalisation and final target validation happen after every transformation. If they do not, an attacker can move from approved user content to system files, config files and process-linked resources with no container escape at all.
Practical implication: Validate the resolved target path after normalisation and reject any request that escapes the intended file boundary.
Why environment variables become an NHI secret problem
Many AI and containerised applications store database strings, API keys, signing material and service tokens in environment variables. That is convenient for deployment, but it also means secrets become part of the process environment and can be exposed through /proc/self/environ or equivalent process views. This is an NHI governance issue because service credentials are now bound to runtime processes, not just secret stores. If the access model treats those values as ordinary configuration, teams lose visibility over who can read, rotate or revoke them when the process context is exposed.
Practical implication: Move high-value service credentials out of process environment variables wherever feasible and govern them as NHI secrets.
Threat narrative
Attacker objective: The attacker wants to extract process secrets and runtime artifacts from an AI container without needing a full container escape.
- Entry occurs through a file retrieval endpoint that accepts attacker-controlled path input and can be directed at /proc paths.
- Credential and secret exposure follows when the attacker reads /proc/self/environ, process file descriptors or the executable image to recover runtime data.
- Escalation comes from using the recovered binaries, tokens or connection details to map the application, locate additional secrets and expand access.
- Impact is theft of credentials, binaries and internal configuration from what developers assumed was an isolated AI container.
Breaches seen in the wild
- 12,000 secrets in LLM training data: Truffle Security found 11,908 live API keys and passwords hard-coded in web pages captured by Common Crawl, a dataset used to train LLMs.
- DeepSeek database exposure 2025: An unauthenticated DeepSeek ClickHouse database exposed over a million log lines with plaintext chat history and API keys in 2025.
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
Container isolation is not a secret boundary: AI teams keep overestimating what containerisation actually protects. A container reduces some classes of blast radius, but it does not neutralise path traversal, process introspection or runtime secret exposure. For practitioners, the control question is whether the application can ever reach data it was not explicitly supposed to read.
/proc abuse is a governance failure, not just an implementation bug: once file download logic can traverse into /proc, the platform has lost separation between user content and process state. That turns environment variables, binaries and sockets into reachable assets, which is exactly where NHI governance intersects with application security. The practical lesson is that runtime secrets must be controlled as privileged machine identities, not treated as harmless config.
Path traversal becomes far more dangerous in AI platforms because the process context is richer: LLM and agent workflows often need tools, file access and data processing privileges by design. That increases the amount of sensitive material present in memory, on disk and in environment variables. The named concept here is runtime secret leakage through process introspection, and it means defenders must secure the process view, not only the container image.
AI security programmes should stop assuming that ‘isolated’ means ‘contained enough’: the article shows how easily a narrow download feature becomes a disclosure channel for credentials and internal architecture. This is a sign that AI application reviews need stronger file boundary testing, secrets governance and process-level hardening. Practitioners should treat runtime access paths as first-class risk surfaces.
File access controls and secrets controls now share the same failure mode: when one endpoint can read arbitrary paths, it can often reach the very material that was supposed to make the system secure. That linkage collapses the comfort of layered defences if secrets are still exposed through environment variables or open file descriptors. Teams should assume that any file-read weakness can become an identity and secrets incident.
From our research library:
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to the State of Secrets in AppSec.
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- Read next: AI Agent Observability, Audit and Incident Response Guide
What this signals
Runtime secret leakage through process introspection: this is the control failure AI teams now need to name explicitly. A container can still be a leaky runtime if the application allows reads into process state, so the boundary to defend is the file-resolution path and not the container label. Practitioners should treat /proc access tests as part of AI application assurance.
For readers running LLM workloads, the signal is simple: secrets governance must move closer to issuance and file access controls must move closer to runtime. If API keys, tokens and configuration secrets still live in process environments, one traversal bug can expose them all.
Use the OWASP NHI lens where it fits: service credentials inside AI containers are still non-human identities, and their lifecycle does not become safer just because the workload is containerised. The control question is whether those secrets can be reached, copied and reused from the running process.
For practitioners
- Harden file retrieval endpoints Reject any user-supplied path that is not resolved into an approved, normalised, final target under a strict allowlist. Test for traversal into /proc, hidden directories and symlink chains.
- Remove secrets from process environments Stop relying on environment variables for API keys, signing secrets and database credentials where a dedicated secret store or runtime injection control is available. Assume /proc/self/environ can be exposed if file read logic fails.
- Test for /proc exposure in AI container reviews Add negative tests that attempt reads from /proc/self/environ, /proc/[PID]/fd and /proc/[PID]/exe during security verification of containerised AI services.
- Separate user files from runtime state Store user-uploaded content and application runtime assets in distinct paths and mount points so a traversal bug cannot bridge the two. Audit any helper that converts logical names into file system paths.
- Treat containerisation as one control, not the control Use container isolation to reduce blast radius, but pair it with input validation, file boundary enforcement and secrets lifecycle governance for AI workloads.
Key takeaways
- AI containers can still expose sensitive runtime material when file downloads are allowed to traverse into /proc.
- The article shows that environment variables, binaries and open file descriptors can become reachable without a full container escape.
- Security teams need to pair container isolation with strict path validation, secrets governance and runtime file boundary testing.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP API Security Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST SP 800-53 Rev 5 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 — Secret Leakage | The article centres on secrets exposed through /proc and leaked from runtime environments. |
| NHI-07 — Long-Lived Secrets | Environment variables and reusable tokens persist long enough to be captured through traversal abuse. | |
| NHI-05 — Overprivileged NHI | The attack succeeds when AI workloads can read more process state than their function requires. | |
| Recommendation — Scan AI workloads for exposed secrets in process environments and revoke anything reachable through runtime file access. Replace long-lived runtime secrets with short-lived credentials and reduce their exposure inside containers. Restrict service account and workload access so AI containers cannot read unnecessary files or secret paths. | ||
| OWASP API Security Top 10 | API8 — Security Misconfiguration | The vulnerable retrieve endpoint reflects a misconfigured file-access API that exposes unintended paths. |
| Recommendation — Harden file-access APIs so resolved paths cannot escape intended storage boundaries. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Least privilege should prevent a file access feature from reaching process state and runtime secrets. |
| Recommendation — Apply least privilege to file retrieval services so they cannot access /proc or other sensitive runtime resources. | ||
| CIS Controls v8 | CIS-5 — Account Management | The article's secret exposure problem affects service accounts and runtime credentials used by AI workloads. |
| Recommendation — Review account scope and remove unnecessary access from the service identities used by AI containers. | ||
| MITRE ATT&CK | TA0007;TA0009 — Discovery; Collection | The attacker enumerates processes and collects binaries and secrets from runtime file paths. |
| Recommendation — Map file traversal and /proc enumeration to Discovery and Collection techniques in detection coverage. | ||
Key terms
- Path Traversal: A bug where crafted path segments such as ../ allow input to escape an intended directory boundary. In practice, it turns a normal file operation into a boundary break, which is especially dangerous when the affected service runs with non-human identity privileges and touches production data or secrets.
- Proc Filesystem: The proc filesystem is a Linux virtual filesystem that exposes live process information such as open file descriptors, command lines, and environment variables. It is useful for administration but dangerous when application-level file access flaws let attackers read process details they should never see.
- Runtime Secret Visibility: Runtime secret visibility is the ability to observe when and where secrets are accessed while systems are actively running. It matters because static inventories alone do not show whether a tool, workflow, or workload is reading credentials in memory, files, or logs.
- File Descriptor Enumeration: File descriptor enumeration is the practice of discovering which files, sockets or pipes a process has open by inspecting its descriptor table. Attackers use it to bypass blind path guessing and locate sensitive files that the application can already access, including configs, sockets and hidden data sources.
Deepen your knowledge
NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, secrets management and workload identity for practitioners building secure access models. It is suited to security teams that need to govern service credentials as part of a broader identity programme.
Published by the NHIMG editorial team on August 20, 2026.
Updated on October 12, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org