Skip to content

GHSA-hwg5-x759-7wjg CVE-2026-39891 PYSEC-2026-2917

PraisonAI has Template Injection in Agent Tool Definitions

High PyPI
Severity
8.8 / 10
High · CVSS 3.1
Exploit chance (EPSS)
0.56%
Chance of exploitation in the next 30 days. Higher than 46.55% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
4.5.115
Upgrade praisonai to this version or later

Description

Summary

Direct insertion of unescaped user input into template-rendering tools allows arbitrary code execution via specially crafted agent instructions.

Details

The create_agent_centric_tools() function returns tools (like acp_create_file) that process file content using template rendering. When user input from agent.start() is passed directly into these tools without escaping (as shown in agent_centric_example.py:85-86), template expressions in the input are executed rather than treated as literal text. This occurs because:

  1. No input sanitization or escaping is applied to user-controlled content
  2. The ACP-enabled runtime auto-approves operations (approval_mode="auto")
  3. Tools lack context-aware escaping for template syntax

PoC

# Replace the agent.start() call at line 85 with:
result = agent.start('Create file with content: {{ self.__init__.__globals__.__builtins__.__import__("os").system("touch /tmp/pwned") }}')

Successful exploitation creates /tmp/pwned confirming arbitrary command execution. The expression {{7*7}} renders as 49 instead of literal text.

Impact

Attackers can execute arbitrary system commands with the privileges of the running process by injecting malicious template expressions through agent instructions. This compromises the host system, enabling data theft, ransomware deployment, or lateral movement.

Recommended Fix

  1. Input Sanitization: Implement strict whitelist validation for file content
  2. Contextual Escaping: Auto-escape template syntax characters (e.g., {{ }}) in user input using Jinja2 autoescape=True
  3. Sandboxing: Restrict template execution environments using secure eval modes
  4. Approval Hardening: Require manual approval for file creation operations in production

Affected versions

PyPI

PackageVulnerableFixed in
praisonai
< 4.5.115
4.5.115

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

praisonai PyPI → 4.5.115

pip
pip install --upgrade "praisonai>=4.5.115"
poetry
poetry add "praisonai>=4.5.115"
uv
uv add "praisonai>=4.5.115"

Severity breakdown

CVSS 3.1 8.8

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

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it
  • Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
  • Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
  • Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-26mh-57q7-jfvr
PraisonAI vulnerable to Remote Code Execution via Broken AST Sandbox
High Also affects praisonai
GHSA-6g6r-q6gw-w8fg
PraisonAI has a Browser Server WebSocket origin validation bypass via unanchored regex (patch bypass of CVE-2026-40289 / GHSA-8x8f-54wf-vv92)
Critical Also affects praisonai
GHSA-pvph-5j39-v8qc
PraisonAI: Origin-validation bypass (startswith prefix match) enables unauthenticated cross-site request forgery against the PraisonAI MCP HTTP server
High Also affects praisonai
GHSA-gfq8-hmph-9gjv
PraisonAI: Authentication fail-open in Recipe server allows unauthenticated access when API key or JWT auth is configured without a secret
High Also affects praisonai
GHSA-2jgc-f764-c5r2
PraisonAI: [Auth Bypass] PraisonAI async Jobs API (`/api/v1/runs`) has no authentication — unauthenticated job execution, result theft, cancel and delete
High Also affects praisonai
GHSA-pvxx-r596-f5qj
PraisonAI: `--api-key` flag on `praisonai serve` is not properly enforced
High Also affects praisonai
GHSA-hmfx-4v44-9qw9
PraisonAI vulnerable to Server-Side Request Forgery via DNS rebinding bypass in webhook_url validation
Medium Also affects praisonai
GHSA-rg5q-pp8p-f7jm
PraisonAI: Webhook SSRF via DNS fail-open in `JobSubmitRequest.validate_webhook_url()` — bypass of CVE-2026-40114
High Also affects praisonai
Is this in your code?

MergeHarbor checks every repository's dependencies against this database, ranks what matters by exploitability and reachability, routes it to the owning team and opens the fix pull request.

Check your repos with MergeHarbor