🔒 Security advisory: AG2 v0.13.2 patches GHSA-9fvw-gr53-m7fw, a code injection vulnerability affecting ContextExpression in prior versions. If you’re running AG2 in production, upgrade immediately.

AG2 v0.13.2: Evaluations, New Clients, and a Critical Security Patch

AutoGen — now officially maintained as AG2 — shipped v0.13.2 on May 29, 2026, with three notable additions landing in one release: a beta evaluation framework for grading agent outputs, two new V2 LLM clients expanding model access, and a critical security fix that should make upgrading non-optional for anyone running AG2 in production.

Let’s take each in order, starting with what’s most urgent.

Security Fix: Code Injection in ContextExpression (Upgrade Required)

CVE ID: GHSA-9fvw-gr53-m7fw
Severity: Critical
Affected component: ContextExpression.evaluate() in the Classic Framework

The vulnerability is a code injection flaw discovered and reported by security researcher William Goodfellow. Here’s the mechanics: when ContextExpression evaluated string context variables, values were formatted as f"'{var_value}'" and passed directly to Python’s eval(). An attacker who could control a context variable value could break out of the string literal and inject arbitrary Python expressions.

A minimal example of the exploit pattern:

# Malicious context variable value:
"' or MARK('PWNED') or '"

# Which evaluates to:
eval("'' or MARK('PWNED') or '' == 'safe'")
# MARK('PWNED') executes

The fix in v0.13.2 (PR #2891) properly escapes string values before interpolation:

formatted_value = "'" + var_value.replace("\\", "\\\\").replace("'", "\\'") + "'"

A regression test was added to verify that injection attempts no longer execute.

Who is affected: Any deployment where untrusted data could flow into context variables used in ContextExpression evaluations. This includes multi-agent setups where agent outputs are used as context for downstream expressions, or where user inputs are passed into context variable chains.

What to do: Upgrade to v0.13.2 now:

pip install --upgrade ag2
# or
pip install --upgrade autogen-agentchat

Verify your version:

python -c "import autogen; print(autogen.__version__)"
# Expect: 0.13.2

Agent Evaluations: Now in Beta

The more forward-looking addition in this release is the Agent Evaluations beta framework (autogen.beta.eval). This is AG2’s native answer to the growing need for structured testing and benchmarking of agentic pipelines.

Evaluation of multi-agent systems has been a persistent pain point in the field. Unlike traditional software tests where you assert specific outputs, agent evaluation requires handling variability, multi-step task completion, and qualitative judgment about whether an agent actually accomplished something useful.

The beta eval framework provides tooling for defining evaluation tasks, running agent pipelines against them, and scoring outputs — the first step toward reproducible benchmarking of AG2-based agent systems.

Details are limited since this is a beta feature, but the inclusion in a stable point release suggests the AG2 team is ready for broader testing and feedback. Explore the autogen.beta.eval module in the updated package and check the AG2 documentation for examples.

Two New V2 LLM Clients

v0.13.2 also adds two new V2 LLM clients:

  • Anthropic client — Direct integration with Anthropic’s models (including Claude Opus 4.7 and 4.8) without needing to go through an OpenAI-compatible wrapper
  • Amazon Bedrock client — Native Bedrock integration for accessing Anthropic models (and potentially other Bedrock-hosted models) through the managed AWS service

These additions matter for enterprise AG2 users who want to use Claude models in their multi-agent pipelines without routing through third-party compatibility layers. The native clients should offer better reliability, more accurate error handling, and access to provider-specific features.

Shell Operator Hardening

As part of the Classic Framework security hardening in this release, shell operators are now blocked in readonly/allowed-command mode. This closes a potential vector where shell metacharacters could be used to chain commands in contexts where only a restricted command set should be permitted.

The Broader AG2 Trajectory

v0.13.2 illustrates where AG2 development is focused right now: evaluation infrastructure, model access breadth, and security hardening. The beta eval framework in particular signals that the project is moving toward making agentic pipelines more rigorous and testable — which is exactly what production deployments need.

TinyFish tools integration also ships in this release (additional tooling for constrained/lightweight agent scenarios), alongside the usual bug fixes.

If you’re running AG2 in any production capacity, the GHSA-9fvw-gr53-m7fw patch alone makes this upgrade mandatory. Do it today.

Sources

  1. AG2 Releases — GitHub
  2. GHSA-9fvw-gr53-m7fw Security Advisory — GitHub Security Advisories
  3. Fix PR #2891 — ag2ai/ag2

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260530-2000

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