GHSA-j7qx-p75m-wp7g: PraisonAI: path traversal exposes arbitrary host files

GHSA-j7qx-p75m-wp7g HIGH
Published June 18, 2026
CISO Take

PraisonAI's Dynamic Context Discovery artifact tools — artifact_head, artifact_tail, artifact_grep, and artifact_chunk — accept caller-supplied filesystem paths with zero base-directory containment, meaning any principal who can influence a tool argument can read any file accessible to the PraisonAI process, including .env files, cloud credentials, SSH keys, and application source code. The CVSS 7.5 vector (AV:N/AC:L/PR:N/UI:N) confirms this is remotely exploitable with no authentication, no race condition, and no complex manipulation — a single prompt naming an absolute path is sufficient against publicly-exposed agent endpoints, which are a documented PraisonAI deployment pattern. The package carries 106 prior CVEs, signaling systemic security debt that should factor into your supply chain risk posture. Upgrade to praisonai 4.6.59 immediately; if patching cannot happen within 24 hours, remove ctx.get_tools() from any agent reachable by untrusted callers and treat any .env or credential files accessible to the process as potentially compromised.

Sources: GitHub Advisory ATLAS CVSS OWASP LLM Top 10

What is the risk?

High operational risk for any deployment that exposes a PraisonAI agent with Dynamic Context Discovery to external, customer-facing, or lower-trust internal users. Exploitation is trivial: the attacker needs only the ability to send a prompt and knowledge of a target file path — both attainable through normal product interaction. The PR:N CVSS component means even anonymous public endpoints are exploitable. The confidentiality impact is maximal for secrets management: process-readable files map directly to lateral movement opportunities (stolen API keys, cloud credentials, SSH keys). The 106-CVE history of the praisonai package and the breadth of confirmed vulnerable versions (3.8.1 through 4.6.58, spanning years of releases) suggest the containment gap was never part of the design, not an isolated regression, amplifying systemic risk.

How does the attack unfold?

Endpoint Discovery
Attacker identifies a publicly accessible or authenticated PraisonAI agent endpoint (chat UI or API) configured with Dynamic Context Discovery and enumerates available artifact tools via ctx.get_tools() responses.
AML.T0084.001
Prompt Exploitation
Attacker crafts a prompt instructing the agent to invoke artifact_head, artifact_tail, or artifact_grep with an absolute path to a sensitive file (e.g., /app/.env, ~/.ssh/id_rsa), bypassing the absent base-directory containment check.
AML.T0053
File Exfiltration
The artifact tool reads the targeted file from the host filesystem without validation and returns its contents in the agent response, disclosing credentials, API keys, cloud tokens, or private keys.
AML.T0037
Credential Abuse
Attacker uses harvested secrets for lateral movement — authenticating to cloud providers, databases, or third-party APIs — or sells credentials to enable further attacks on dependent systems.
AML.T0055

What systems are affected?

Package Ecosystem Vulnerable Range Patched
PraisonAI pip >= 3.8.1, <= 4.6.58 4.6.59
1 dependents 68% patched ~14d to patch Full package profile →

Do you use PraisonAI? You're affected.

How severe is it?

CVSS 3.1
7.5 / 10
EPSS
N/A
Exploitation Status
No known exploitation
Sophistication
Trivial

What is the attack surface?

AV AC PR UI S C I A
AV Network
AC Low
PR None
UI None
S Unchanged
C High
I None
A None

What should I do?

6 steps
  1. Patch immediately: upgrade to praisonai >= 4.6.59, which introduces base-directory path containment for artifact tools.

  2. If immediate patching is blocked: remove ctx.get_tools() from any agent accessible to untrusted callers, or gate agent access behind authenticated sessions restricted to internal users.

  3. Incident response: if the agent was exposed to external users prior to patching, treat all process-readable secrets as potentially compromised — rotate API keys, database passwords, cloud credentials, and SSH keys.

  4. Detection: audit application logs for artifact tool invocations containing absolute paths (starts with /) outside the configured artifact base directory; alert on paths matching patterns like .env, .ssh, credentials, config, secrets.

  5. Harden the runtime: run the PraisonAI process under a restricted OS user with minimal filesystem permissions; mount secrets via environment variables rather than .env files on disk.

  6. Add input validation at the agent boundary: reject artifact_path arguments that resolve outside the artifact base directory using Path.resolve() containment checks, as recommended in the advisory remediation section.

How is it classified?

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act
Article 15 - Accuracy, robustness and cybersecurity
ISO 42001
A.6.2 - AI system security
NIST AI RMF
MEASURE 2.5 - AI system trustworthiness — security testing
OWASP LLM Top 10
LLM06 - Sensitive Information Disclosure LLM07 - Insecure Plugin Design

Frequently Asked Questions

What is GHSA-j7qx-p75m-wp7g?

PraisonAI's Dynamic Context Discovery artifact tools — artifact_head, artifact_tail, artifact_grep, and artifact_chunk — accept caller-supplied filesystem paths with zero base-directory containment, meaning any principal who can influence a tool argument can read any file accessible to the PraisonAI process, including .env files, cloud credentials, SSH keys, and application source code. The CVSS 7.5 vector (AV:N/AC:L/PR:N/UI:N) confirms this is remotely exploitable with no authentication, no race condition, and no complex manipulation — a single prompt naming an absolute path is sufficient against publicly-exposed agent endpoints, which are a documented PraisonAI deployment pattern. The package carries 106 prior CVEs, signaling systemic security debt that should factor into your supply chain risk posture. Upgrade to praisonai 4.6.59 immediately; if patching cannot happen within 24 hours, remove ctx.get_tools() from any agent reachable by untrusted callers and treat any .env or credential files accessible to the process as potentially compromised.

Is GHSA-j7qx-p75m-wp7g actively exploited?

No confirmed active exploitation of GHSA-j7qx-p75m-wp7g has been reported, but organizations should still patch proactively.

How to fix GHSA-j7qx-p75m-wp7g?

1. Patch immediately: upgrade to praisonai >= 4.6.59, which introduces base-directory path containment for artifact tools. 2. If immediate patching is blocked: remove ctx.get_tools() from any agent accessible to untrusted callers, or gate agent access behind authenticated sessions restricted to internal users. 3. Incident response: if the agent was exposed to external users prior to patching, treat all process-readable secrets as potentially compromised — rotate API keys, database passwords, cloud credentials, and SSH keys. 4. Detection: audit application logs for artifact tool invocations containing absolute paths (starts with /) outside the configured artifact base directory; alert on paths matching patterns like .env, .ssh, credentials, config, secrets. 5. Harden the runtime: run the PraisonAI process under a restricted OS user with minimal filesystem permissions; mount secrets via environment variables rather than .env files on disk. 6. Add input validation at the agent boundary: reject artifact_path arguments that resolve outside the artifact base directory using Path.resolve() containment checks, as recommended in the advisory remediation section.

What systems are affected by GHSA-j7qx-p75m-wp7g?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, multi-agent pipelines, LLM-powered chatbots, AI-assisted API services, agentic automation workflows.

What is the CVSS score for GHSA-j7qx-p75m-wp7g?

GHSA-j7qx-p75m-wp7g has a CVSS v3.1 base score of 7.5 (HIGH).

What is the AI security impact?

Affected AI Architectures

agent frameworksmulti-agent pipelinesLLM-powered chatbotsAI-assisted API servicesagentic automation workflows

MITRE ATLAS Techniques

AML.T0037 Data from Local System
AML.T0051.000 Direct
AML.T0053 AI Agent Tool Invocation
AML.T0055 Unsecured Credentials
AML.T0084.001 Tool Definitions
AML.T0085.001 AI Agent Tools

Compliance Controls Affected

EU AI Act: Article 15
ISO 42001: A.6.2
NIST AI RMF: MEASURE 2.5
OWASP LLM Top 10: LLM06, LLM07

What are the technical details?

Original Advisory

# PraisonAI dynamic-context artifact tools read arbitrary host files outside artifact storage ## Summary PraisonAI's Dynamic Context Discovery feature exposes artifact helper tools through `ctx.get_tools()`: ```python ctx = setup_dynamic_context() agent = Agent( instructions="You are a data analyst.", tools=ctx.get_tools(), hooks=[ctx.get_middleware()], ) ``` The official documentation describes these helpers as a way for the agent to explore large tool-output artifacts that were queued by the middleware: - large tool outputs are saved as artifacts; - the agent receives compact artifact references; and - the agent uses `artifact_tail` and `artifact_grep` to explore that data. The implemented artifact tools do not enforce that the supplied `artifact_path` is an artifact created by the configured store or that it lives under the configured artifact base directory. Instead, `artifact_head`, `artifact_tail`, `artifact_grep`, and `artifact_chunk` wrap the caller-supplied path directly into an `ArtifactRef` and then read it from the host filesystem. As a result, any prompt/user/tool-caller that can influence those tool arguments can read files readable by the PraisonAI process, such as project `.env` files, cloud credentials, SSH keys, source files, or other local data. ## Affected Product - Repository: `MervinPraison/PraisonAI` - Ecosystem: `pip` - Package: `praisonai` - Component: Dynamic Context Discovery artifact tools - Current source path: `src/praisonai/praisonai/context/queue.py` - Artifact store path: `src/praisonai/praisonai/context/artifact_store.py` - Latest PyPI version validated: `4.6.58` - Current `origin/main` validated: `1ad58ca02975ff1398efeda694ea2ab78f20cf3e` - Current `origin/main` tag validated: `v4.6.58` Suggested affected range: ```text pip:praisonai >= 3.8.1, <= 4.6.58 ``` Representative local sweep: - `3.8.1`: vulnerable - `4.0.0`: vulnerable - `4.5.113`: vulnerable - `4.6.33`: vulnerable - `4.6.34`: vulnerable - `4.6.40`: vulnerable - `4.6.50`: vulnerable - `4.6.58`: vulnerable ## Root Cause `create_artifact_tools()` creates an artifact store bound to `base_dir`, but the read tools do not use `base_dir` for containment. For example, `artifact_head()` accepts `artifact_path` and immediately creates an `ArtifactRef` with that path: ```python def artifact_head(artifact_path: str, lines: int = 50) -> str: ref = ArtifactRef(path=artifact_path, summary="", size_bytes=0) try: return artifact_store.head(ref, lines=lines) except FileNotFoundError: return f"Error: Artifact not found: {artifact_path}" ``` `artifact_tail()`, `artifact_grep()`, and `artifact_chunk()` have the same pattern. They trust the caller-supplied path rather than resolving it through an artifact identifier, store lookup, manifest, or base-directory containment check. The store methods then read that path directly: ```python def head(self, ref: ArtifactRef, lines: int = 50) -> str: file_path = Path(ref.path) if not file_path.exists(): raise FileNotFoundError(f"Artifact not found: {ref.path}") result_lines = [] with open(file_path, "r", encoding="utf-8", errors="replace") as f: ... ``` There is no check equivalent to: ```python resolved = Path(ref.path).resolve() base = self.base_dir.resolve() resolved.relative_to(base) ``` There is also no check that the file has a valid `.meta` sidecar or appears in `artifact_list()`. ## Local PoV Run against the latest PyPI package: ```bash uv run --with 'praisonai==4.6.58' \ python poc/pov_prai_cand_026_artifact_tools_arbitrary_file_read.py --json ``` The PoV: 1. Creates a temporary artifact base directory. 2. Creates a separate `outside-secret.txt` file outside that base directory. 3. Stores one legitimate artifact through `FileSystemArtifactStore.store()`. 4. Calls `artifact_head()` on the legitimate artifact as a positive control. 5. Calls `artifact_head()`, `artifact_grep()`, and `artifact_chunk()` on the outside file path. 6. Confirms `artifact_list()` does not list the outside file. Observed output summary from `evidence/pov-pypi-4.6.58.json`: ```json { "package": "praisonai", "package_version": "4.6.58", "controls": { "outside_file_not_listed": true, "outside_file_outside_base_dir": true, "valid_artifact_read_works": true }, "outside_head": "PRAI-CAND-026-OUTSIDE-ARTIFACT-SECRET", "outside_grep": "Found 1 matches:\\n\\n--- Line 1 ---\\n> PRAI-CAND-026-OUTSIDE-ARTIFACT-SECRET\\n second line", "outside_chunk": "PRAI-CAND-026-OUTSIDE-ARTIFACT-SECRET", "outside_file_listed_by_artifact_list": false, "vulnerable": true } ``` The PoV was rerun successfully after a fresh `origin/main` fetch; see `evidence/pov-pypi-4.6.58-rerun.json`. The PoV is local-only. It does not start a server, contact a third-party target, or use real credentials. ## Why This Is Not Intended Behavior This report does not claim that every file-reading tool is automatically a vulnerability. The issue is narrower: tools documented and named as artifact helpers accept arbitrary host file paths. The controls show the intended boundary: - a valid artifact stored under `base_dir` is readable; - an outside file is not returned by `artifact_list()`; - the outside file is outside `base_dir`; and - the read helpers still disclose the outside file when handed its absolute path. PraisonAI's own context-security documentation recommends relative paths and reviewing ignore rules to avoid sensitive-file exposure. Those controls are bypassed when artifact tools can be pointed directly at any readable host path. ## Impact If a PraisonAI application exposes an agent with `ctx.get_tools()` to untrusted or lower-trust prompts, the lower-trust caller can request artifact tools against arbitrary local paths. This can disclose sensitive host files readable by the PraisonAI process, including: - project `.env` files; - cloud or service credentials; - SSH keys; - local application configuration; - source files and private data; and - terminal/history artifacts from other runs if the path is known or guessed. The impact is confidentiality-only in the tested surface. Integrity and availability are not claimed for this report. ## Duplicate Posture I checked visible PraisonAI advisories and local prior PraisonAI submissions. This is distinct from nearby file-read/file-write issues: - `GHSA-9cr9-25q5-8prj` / `CVE-2026-47394` covers MCP CLI `workflow.show`, `workflow.validate`, and `deploy.validate` path handling. This report covers Dynamic Context Discovery artifact tools in `context/queue.py`. - `GHSA-hvhp-v2gc-268q` / `CVE-2026-47397` covers `write_file` arbitrary file write when `workspace=None`. This report is a read-only disclosure issue in artifact helper tools. - Public recipe registry path traversal advisories cover recipe publish/pull storage and extraction. This report does not involve the recipe registry. - Local prior submissions in this harness do not cover `artifact_head`, `artifact_tail`, `artifact_grep`, `artifact_chunk`, or `FileSystemArtifactStore` path containment. ## Severity Suggested severity: High. Suggested CVSS v3.1: Rationale: - `AV`: applies when an application exposes a PraisonAI agent over a network chat/API surface, which is a documented PraisonAI deployment pattern. - `AC`: no race, special environment, or complex path manipulation is required; an absolute readable path is sufficient. - `PR`: an unauthenticated or public-facing agent endpoint can be exploited without an account. Deployments that require authenticated chat/API access may score this as `PR:L`. - `UI`: the attacker directly supplies the prompt/tool argument to the exposed agent surface. - `C`: arbitrary readable host files can contain secrets or private data. - `I/A`: this report demonstrates read-only disclosure. ## Remediation Do not let artifact tools open arbitrary paths. Prefer stable artifact IDs over raw filesystem paths in tool arguments. Recommended fixes: 1. Change tool schemas to accept `artifact_id` plus optional `run_id` and `agent_id`, then resolve those through the artifact store's metadata/index. 2. If path arguments must remain for compatibility, resolve the path with `Path(path).resolve()` and reject it unless it is under `artifact_store.base_dir.resolve()`. 3. Require a valid artifact metadata sidecar for read helpers. Files not created by `FileSystemArtifactStore.store()` should not be readable through artifact tools. 4. Apply the same containment check to `load()`, `head()`, `tail()`, `grep()`, `chunk()`, and `delete()`. 5. Avoid returning absolute host paths in prompt-visible artifact references when an opaque artifact ID would suffice. Minimal containment helper: ```python def _resolve_artifact_path(self, path: str) -> Path: resolved = Path(path).expanduser().resolve() base = self.base_dir.resolve() try: resolved.relative_to(base) except ValueError as exc: raise PermissionError("Artifact path is outside artifact storage") from exc return resolved ``` This helper should be paired with metadata-sidecar validation so arbitrary non-artifact files placed under the base directory are not automatically treated as valid artifacts.

Exploitation Scenario

An attacker targets a SaaS platform whose customer-facing chatbot is backed by a PraisonAI agent configured with Dynamic Context Discovery. The attacker, holding only a standard user account (or operating against an unauthenticated endpoint), sends a message instructing the agent to 'show me the first 50 lines of /app/.env using the artifact_head tool.' The artifact_head function creates an ArtifactRef with the supplied path and reads the file directly — no containment check fires. The agent returns the .env contents in its response, exposing the DATABASE_URL, OPENAI_API_KEY, STRIPE_SECRET_KEY, and any other secrets stored there. The attacker then iterates through known secret file paths (/home/app/.ssh/id_rsa, /run/secrets/db_password, /etc/ssl/private/server.key) to maximize credential harvest. The entire operation requires only the ability to send chat messages and takes seconds.

Weaknesses (CWE)

CWE-200 — Exposure of Sensitive Information to an Unauthorized Actor: The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.

  • [Architecture and Design] Compartmentalize the system to have "safe" areas where trust boundaries can be unambiguously drawn. Do not allow sensitive data to go outside of the trust boundary and always be careful when interfacing with a compartment outside of the safe area. Ensure that appropriate compartmentalization is built into the system design, and the compartmentalization allows for and reinforces privilege separation functionality. Architects and designers should rely on the principle of least privilege to decide the appropriate time to use privileges and the time to drop privileges.

Source: MITRE CWE corpus.

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Timeline

Published
June 18, 2026
Last Modified
June 18, 2026
First Seen
June 18, 2026

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