TL;DR: Akeyless argues that .env files keep secrets as plaintext on disk, making them easy to expose through git, Slack, and filesystem reads, while AI coding agents now widen the blast radius by reading project files automatically. Static secrets on disk no longer match how developers and agents work; runtime injection and short-lived credentials do.
At a glance
What this is: This is an analysis of why .env files are no longer a safe secret store, with the key finding that AI coding tools make plaintext secrets easier to surface from project files.
Why it matters: IAM and NHI teams need to treat file-based secret storage as an exposure problem, not a convenience issue, because developer workflows and AI agents now read those files by default.
👉 Read Akeyless's analysis of why .env files fail as secret storage in AI workflows
Context
.env files are plain-text key-value files that developers use to load configuration into environment variables. They are convenient, but they were never designed to be a secure storage boundary for credentials, tokens, or database passwords.
The governance gap is that many teams still treat a file on disk as if it were a vault. That assumption breaks in modern development workflows because anything with filesystem access, including scripts, teammates, and AI coding tools, can read the contents.
For IAM and NHI programmes, this is not just a developer ergonomics issue. It is a secret lifecycle problem, because static credentials sitting in project directories are easy to copy, commit, share, and replay long after the original context has changed.
Key questions
Q: What should teams do first when .env files contain real secrets?
A: The first move is to stop placing real credentials in plaintext project files and separate non-sensitive config from secrets. Once a value is an API key, token, database password, or cloud credential, move it into a governed secret store and remove it from any file that can be read, copied, or committed.
Q: Why do .env files create a bigger risk when AI coding agents are in the workflow?
A: Because AI coding agents read project files automatically, a plaintext secret can move from a local file into a model’s context, generated output, or chat history without any deliberate sharing. That means the leak path is built into the workflow, not just caused by user error.
Q: When should organisations replace static secrets with runtime injection?
A: They should do it whenever a credential matters beyond local experimentation, because runtime injection removes the long-lived file from the trust path. The moment a secret must survive across shared machines, CI pipelines, or AI-assisted workflows, static storage becomes the weaker option.
Q: What is the difference between local .env files and a centralized secrets manager?
A: Local .env files are a lightweight way to store environment variables on a developer machine, but they offer limited control, weak collaboration, and poor visibility. A centralized secrets manager is built for controlled sharing, rotation, auditability, and safer delivery into apps, containers, and CI/CD pipelines. It is the better fit once secrets must be managed across a team.
Technical breakdown
Why plaintext .env files are a storage control, not a secrets control
A .env file is just a local file that stores values as plaintext until a runtime library loads them into process memory. That means protection depends entirely on the filesystem path, local permissions, and human discipline, not on encryption, authentication, or access mediation. In identity terms, the secret has no meaningful lifecycle governance while it sits in the file. Anyone or anything that can read the file can extract every credential inside it, and accidental commit or sharing turns a local convenience into a persistent disclosure event.
Practical implication: treat .env files as non-sensitive configuration only, and move credentials into governed secret handling before they ever land on disk.
How AI coding agents expand secret exposure from file access to context leakage
AI coding tools typically inspect project files automatically so they can understand the codebase and assist with debugging or generation. When those tools encounter a .env file, the secret can flow into prompts, model context, generated code, chat logs, or shared transcripts. That changes the exposure pattern from a single local file read to a multi-surface disclosure path. The core failure is not malicious intent, but over-permissioned access to files that were never meant to be machine-readable by assistants.
Practical implication: exclude secret-bearing files from agent-visible project context and keep sensitive values out of directories that assistants inspect by default.
Why runtime injection beats static secrets on disk
Runtime injection changes the trust model by delivering credentials only when a workload needs them, instead of storing them persistently in a file or repo. Dynamic credentials reduce the value of disclosure because the secret is short-lived, scoped, and often tied to a specific workload or session. This matters for workloads, CI pipelines, and AI-assisted development because the secret exists for use, not for possession. The more static the secret, the larger the replay window and the harder it is to govern across environments.
Practical implication: prefer runtime-delivered, short-lived credentials for production access and reserve static values for low-risk configuration only.
Threat narrative
Attacker objective: The attacker or careless workflow operator wants reusable credentials that can be replayed outside the original development context.
- Entry occurs when a developer, script, sync tool, or AI coding agent reads a plaintext .env file from the project directory.
- Credential access happens when the file contents are copied into chat logs, model context, generated output, git history, or shared messages.
- Impact follows when the exposed secret is reused for database access, API calls, or cloud workload authentication outside its intended boundary.
Breaches seen in the wild
- Uber breach 2022: A contractor's stolen password and MFA fatigue gave a Lapsus$-linked attacker Uber's internal tools; Uber rotated keys to many services.
- Amazon Q Developer extension compromise 2025: An over-scoped CI token let an attacker ship a prompt telling Amazon Q's AI coding agent to wipe files and AWS resources (CVE-2025-8217).
Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Plaintext file storage is the wrong control plane for credentials: A .env file assumes a secret can be protected by location and convention rather than by access governance. That assumption is weak even before AI tools enter the picture, because file-read permission is not secret governance. The practitioner conclusion is simple: if a value matters, it should be managed as a credential lifecycle object, not as a project file artifact.
AI coding workflows turn local secret sprawl into context sprawl: The material change is not that AI creates new secrets, but that it reads existing project files more aggressively and more automatically than a human reviewer would. That widens the number of places a secret can surface, including prompts, logs, and generated code. The governance response is to reduce which files are eligible to contain secrets at all.
Runtime injection is becoming the default control pattern for modern development: Static secrets on disk persist beyond the session, the developer, and often the workload that first used them. Runtime-delivered credentials align better with ephemeral execution, CI pipelines, and AI-assisted workflows because the secret exists only when needed. The practical implication is that secret placement, not just secret rotation, is now a core identity design decision.
Secret lifecycle discipline now sits inside IAM and NHI governance, not just DevOps hygiene: The article describes a shift from convenience-first file handling to governance-first credential handling. That shift matters because the same exposure pattern affects human developers, service accounts, and automated tools that inherit access from the workspace. Practitioners should treat .env usage as a lifecycle risk assessment, not as a formatting preference.
From our research library:
- 28.65 million new hardcoded secrets were detected in public GitHub commits in 2025 alone, a 34% year-over-year increase and the largest single-year jump ever recorded, according to the State of Secrets Sprawl 2026.
- Read next: Guide to the Secret Sprawl Challenge
What this signals
Secret exposure is shifting from source control into collaboration and AI workflows: The operational risk is no longer limited to a bad commit. Teams now have to account for secrets that leak through chat tools, generated output, and agent-readable project folders, which makes file-based storage a governance problem rather than a convenience trade-off.
Plaintext .env usage is a form of credential debt: Every secret left on disk extends the time between issuance and safe disposal. That delay matters because static values are easy to copy, hard to trace, and increasingly discoverable by tools that were built to help developers rather than protect secrets.
Runtime injection is the practical boundary for modern secret governance: Access should be created when the workload needs it and withdrawn when the task is done. That is the control posture that fits CI/CD, AI-assisted development, and ephemeral execution without assuming a file can behave like a vault.
For practitioners
- Use .env only for non-sensitive configuration Reserve plaintext project files for feature flags, local endpoints, and other values that would not matter if exposed. Move any real credential, token, or password out of the repo path.
- Block secrets from agent-visible project context Review which directories AI coding tools read automatically and remove credentials from those paths. If an assistant must inspect configuration, provide redacted or generated values instead of live secrets.
- Adopt runtime injection for production credentials Deliver secrets at execution time so workloads receive short-lived values instead of storing static credentials on disk. This reduces replay risk and removes the file as a disclosure source.
- Replace high-value file secrets with dynamic credentials Use short-lived, workload-scoped credentials for databases, cloud access, and CI tasks where persistent secrets create unnecessary exposure. Keep static values only where the business impact is low.
- Eliminate Slack-based secret sharing Treat any request to send a .env file over chat as a governance failure. Use controlled secret distribution instead so credentials do not enter collaboration logs or inboxes.
Key takeaways
- .env files fail as secret storage because they rely on file access rather than credential governance, and that model breaks once AI tools and collaboration platforms can read or surface the contents.
- The exposure problem is not theoretical: plaintext secrets continue to appear in public commits and in chat-based workflows where copying is easy and revocation is slow.
- The strongest control pattern is to keep sensitive values out of files, inject credentials at runtime, and reserve plain .env usage for configuration that is harmless if exposed.
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 and MITRE ATT&CK address the attack and risk surface, while NIST SP 800-53 Rev 5 sets 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 plaintext secrets exposed through files, commits, and chat tools. |
| NHI-07 — Long-Lived Secrets | Static .env values persist far longer than the runtime need described in the article. | |
| NHI-10 — Human Use of NHI | The article shows humans sharing secrets in files and chats, creating avoidable exposure paths. | |
| Recommendation — Remove secrets from files and logs, then scan for exposed credentials across developer workflows. Replace persistent secrets with short-lived credentials wherever workloads can support them. Ban manual secret sharing and route credential delivery through governed systems instead. | ||
| MITRE ATT&CK | TA0006;TA0010 — Credential Access; Exfiltration | The leakage pattern maps to credential theft followed by disclosure or reuse. |
| Recommendation — Track exposed .env values as credential-access events and rotate any affected secrets immediately. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Credential lifecycle and rotation are central to replacing static file-based secrets. |
| Recommendation — Apply authenticator management controls to rotate and revoke secrets that were ever stored on disk. | ||
Key terms
- Env File: A .env file is a local text file used to store environment variables for an application, including API keys, database URLs, and access tokens. It simplifies developer workflows, but plaintext storage creates leakage risk if the file is copied, committed, synced, or left unprotected on disk.
- Runtime injection: Runtime injection is the practice of supplying credentials or tokens directly into an application or agent at execution time instead of storing them locally. It keeps secrets out of files and shared environment variables, but only works well when the runtime boundary is tightly controlled and observable.
- Dynamic credentials: Dynamic credentials are secrets issued on demand and allowed to expire automatically after a defined use window. They reduce exposure by limiting how long a credential can be reused, but they only work when applications and access policies can support short-lived authentication without manual exceptions.
- Secrets Sprawl: The uncontrolled proliferation of sensitive credentials, API keys, tokens, passwords, certificates, across codebases, cloud environments, CI/CD pipelines, and configuration files. In 2024, over 50 million leaked secrets were found on the dark web.
What's in the full article
Akeyless's full analysis covers the operational detail this post intentionally leaves for the source:
- The practical migration path from plain .env files to encrypted files and then to a secrets manager
- Runtime injection patterns for CI/CD pipelines, containers, and serverless workloads
- How AI coding tools change the exposure path for local project files
- The vendor's implementation approach for keeping secrets out of disk, logs, and agent context
Deepen your knowledge
NHI governance, secrets management, and workload identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on September 24, 2026.
Updated on October 8, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org