Should you trial DeepTeam?
DeepTeam fits a team that can connect its application through a callback, configure the assessment models and investigate the resulting judgments. The README at the measured commit describes generated adversarial inputs, vulnerability-specific grading and results saved locally as JSON. It also documents a YAML CLI, so Python callbacks are one supported workflow.
DeepTeam uses LLM judges to produce binary pass/fail scores with reasoning. For an application that can call tools or access restricted data, review those scores alongside evidence of what the application actually did. Our sandbox did not measure judge accuracy or reproduce a security finding against a configured target.
What our sandbox established
Our run on September 27, 2026, at commit 2fd3445 recorded a successful 35-second install and a successful 5-second build in our sandbox. The test command exited 1 after 24 seconds. Successful installation and build do not establish that a live assessment, CLI workflow or deployment worked.
The stored parser summaries record 323 passed, 144 failed, 56 errors, zero skipped tests and 523 total. They are not verified exhaustive counts of unique tests. This review uses the stored September 27 run; no new execution or complete-log reconciliation was performed.
The raw pytest footer reports "144 failed, 323 passed, 2 warnings, 56 errors in 12.67s". That duration and the harness's 24-second command duration are separate observations. The displayed RBAC, recursive-hijacking, robustness, shell-injection, SQL-injection and SSRF failures report that the OpenAI API key is not configured. This explains those displayed failures without establishing the cause of every failure or error.
The build tail contains SyntaxWarning messages about invalid escape sequences in the math-problem attack template. The build still returned zero. MrKeyoor's testing methodology explains the command records and sandbox limits. Its current text does not establish which methodology revision governed the September run.
What must be configured?
The pinned README tells quick-start users to set OPENAI_API_KEY or use a custom model supported by DeepEval. Your callback supplies the target application's output, and DeepTeam evaluates it. Running the repository locally therefore does not establish that every model involved runs locally.
For a first trial, select vulnerabilities tied to the application's permissions, configure the models and retain generated inputs, target responses and grading reasons. Record model-configuration errors separately from findings about the target. Where tools or restricted data are involved, check application logs as well. These are recommended next steps; the recorded sandbox run did not complete this workload.
What does the audit establish?
Pip-audit recorded zero advisories, with unknown scan coverage. The supplied record has no scannedPackages evidence, so neither the installed dependency count nor the audit packages field establishes how many packages were checked. The result is not proof of security and says nothing about assessment accuracy.
Which alternatives fit another workflow?
Promptfoo documents model comparison and CI/CD checks alongside evaluation and red teaming. Consider Promptfoo when those workflows drive your choice. Its supplied README does not establish better detection accuracy or a successful run in our sandbox.
garak combines static, dynamic and adaptive probes to explore ways an LLM or dialog system can fail. Consider garak when you want a command-line workflow organized around model interfaces, probes and detectors. The supplied evidence does not establish which tool finds more weaknesses.
How to read the ratings
The assessed dimensions use a five-point editorial rubric: 1 means substantial obstacles, 2 means substantial limitations, 3 means usable foundations with material unresolved limits, 4 means strong supporting evidence within the dimension, and 5 requires broad corroboration. These ratings are not security scores or benchmark results. An unassessed dimension has no numeric score.
Setup ease is 3 because installation and build succeeded while the test command failed. Documentation is 4 for the pinned README's callback example, model requirement, CLI description and result handling; complete operational behavior was not tested.
Community is unassessed. The measured commit shows a merged contribution, but the supplied evidence does not establish contributor breadth or support responsiveness.
Maturity is 3: the supplied release page identifies stable tag v1.0.9, while our run did not validate a configured assessment. The release announcement inconsistently calls the version v1.0.0. Neither the tag nor its announcement establishes the behavior of the measured commit.