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Introduction:
The overarching theme of Black Hat 2025 was a decisive shift from theoretical risk assessment to operational, actionable security. The consensus among CISOs and security architects is clear: modern defense hinges on achieving deep data visibility, governing AI systems, and integrating security natively into business workflows. This article breaks down the key technical lessons and provides the commands and configurations to implement these strategies immediately.
Learning Objectives:
- Implement agentless deployment and data visibility commands across cloud environments.
- Apply critical security hardening for AI model deployments and data pipelines.
- Integrate security tooling into CI/CD workflows and operationalize risk findings.
You Should Know:
- Agentless Cloud Asset Discovery with AWS CLI and Steampipe
The push for lightweight, agentless deployments means security teams must discover and inventory assets without installing local software. Steampipe uses standard cloud APIs to map your entire environment.
Using AWS CLI for initial service enumeration
aws ec2 describe-instances --query 'Reservations[].Instances[].{ID:InstanceId,Type:InstanceType,State:State.Name,VPC:VpcId,SG:SecurityGroups}' --output table
Using Steampipe to query AWS Config for compliance (SQL-based)
steampipe query "
SELECT
resource_id,
resource_type,
configuration_item_status
FROM
aws_config_configuration_recorder
WHERE
recording_group = 'true';
"
Step-by-step guide: First, ensure your AWS CLI is configured with appropriate read-only IAM credentials (aws configure). The first command lists all EC2 instances. For continuous visibility, install Steampipe (sudo /bin/sh -c "$(curl -fsSL https://steampipe.io/install/install.sh)"). Use its SQL interface to run complex queries against your cloud inventory, such as checking for unencrypted S3 buckets or unpatched instances, aligning with the need for data visibility.
2. AI Model Security Scanning with `robusta`
With AI governance a top concern, scanning models and datasets for vulnerabilities and biases before deployment is critical. The `robusta` CLI tool can scan popular model formats.
Install robusta CLI via pip pip install robusta-cli Scan a Hugging Face model for common vulnerabilities (CVEs) and misconfigurations robusta scan model huggingface:username/model-name --output json > scan_report.json Scan a local dataset for PII leakage before training robusta scan dataset ./training_data/ --checks=pii
Step-by-step guide: After installing the CLI, generate an API key from the Robusta platform. The `scan model` command queries a model repository (like Hugging Face) against a database of known AI/ML vulnerabilities. The `scan dataset` command parses local files (CSV, JSON) to identify potential PII exposure, a key part of securing AI data pipelines.
- Infrastructure as Code (IaC) Security with `tfsec` and `checkov`
Integrating security into development workflows requires scanning IaC templates for misconfigurations before they reach production.
Scan Terraform plans with tfsec
tfsec .
Scan Terraform plans with checkov for a more comprehensive check
checkov -d . --output sarif
Example of a failing Terraform rule: an S3 bucket without encryption
resource "aws_s3_bucket" "example" {
bucket = "my-unencrypted-bucket"
acl = "private" This would cause a tfsec failure
}
Step-by-step guide: Install `tfsec` (brew install tfsec) or `checkov` (pip install checkov). Navigate to your Terraform directory and run the scan. These tools parse the `.tf` files, cross-reference them against hundreds of predefined security policies (like AWS CIS benchmarks), and output a list of vulnerabilities with severity levels, enabling developers to “act on risk.”
4. API Security Testing with `kiterunner` and `ffuf`
Data visibility’s biggest bottleneck is often shadow APIs. Discovering and testing all endpoints is essential.
Bruteforcing API routes with kiterunner (kr) kr kb brute https://target.com --wordlist ./routes.txt Fuzzing API parameters for common injections with ffuf ffuf -w ./parameter-list.txt -u https://api.target.com/v1/user?FUZZ=../../../etc/passwd -fs 400 Testing for Broken Object Level Authorization (BOLA) with curl curl -H "Authorization: Bearer <USER_A_TOKEN>" https://api.target.com/v1/users/1234 curl -H "Authorization: Bearer <USER_B_TOKEN>" https://api.target.com/v1/users/1234 Should return 403
Step-by-step guide: `kiterunner` is superior to traditional scanners for discovering API routes. Use a custom wordlist of API paths. `ffuf` is then used to fuzz discovered endpoints for injection flaws. The manual `curl` tests check for authorization bugs by attempting to access a resource with two different user tokens—a critical test for modern applications.
- Container Hardening Without Agents with `docker-bench-security` and `trivy`
The new norm of agentless deployments extends to containers. These tools provide deep security visibility without a persistent daemon.
Scan a Docker daemon configuration for CIS benchmarks docker run --rm --net host --pid host --userns host --cap-add audit_control \ -v /var/lib:/var/lib \ -v /usr/lib/systemd:/usr/lib/systemd \ -v /etc:/etc --label docker_bench_security \ docker/docker-bench-security Scan a local container image for vulnerabilities with trivy trivy image --severity CRITICAL,HIGH my-app:latest Generate a SBOM (Software Bill of Materials) for visibility trivy image --format cyclonedx my-app:latest > sbom.xml
Step-by-step guide: The `docker-bench-security` command runs a container that performs checks against the host’s Docker runtime, outputting a list of PASS/WARN/INFO items. `Trivy` scans the application image itself for CVEs. Running these in a CI pipeline provides actionable, pre-deployment security feedback, embodying the principle of integration.
- Enforcing Logging and Visibility with `aws logging` and `jq`
You cannot control what you cannot see. Enforcing mandatory logging across cloud services is the first step to data visibility.
Enable AWS CloudTrail logging in a multi-region trail
aws cloudtrail create-trail --name SecurityTrail --s3-bucket-name my-bucket --is-multi-region-trail --enable-log-file-validation
Enable S3 access logging via CLI
aws s3api put-bucket-logging --bucket my-bucket --bucket-logging-status '{
"LoggingEnabled": {
"TargetBucket": "log-bucket",
"TargetPrefix": "s3-access-logs/"
}
}'
Query CloudTrail logs for a specific event (e.g., a failed login) using jq
aws cloudtrail lookup-events --lookup-attributes AttributeKey=EventName,AttributeValue=ConsoleLogin --region us-east-1 | jq '.Events[] | select(.Username == "root") | select(.EventTime > "2025-01-01T00:00:00Z")'
Step-by-step guide: These commands configure foundational logging in AWS. The `jq` command is a powerful JSON processor used to parse and filter the massive volume of log data generated, turning it into actionable intelligence—directly addressing the data visibility bottleneck.
- Mitigating AI Supply Chain Risks with `pip-audit` and `sigstore`
Securing AI from the inside requires hardening its supply chain, from dependencies to model artifacts.
Audit Python dependencies for vulnerabilities (common in AI/ML projects) pip-audit -r requirements.txt Use sigstore/cosign to sign and verify a model artifact file cosign sign --key cosign.key my-model.joblib cosign verify --key cosign.pub my-model.joblib Verify the provenance of a container used for training cosign verify --key release-cosign.pub docker.io/tensorflow/tensorflow:latest-gpu
Step-by-step guide: `pip-audit` checks your Python environment for known vulnerabilities. `Cosign` is used to cryptographically sign model files and verify their integrity and origin, ensuring they haven’t been tampered with in the supply chain. This provides the “operational knowledge” needed to trust AI components.
What Undercode Say:
- Visibility is Actionable, Not Theoretical: The tools and commands provided are not for generating reports that sit on a shelf. They are designed to be integrated into scripts, pipelines, and automated workflows to produce immediate, actionable results.
- Governance is Code: The theme of Black Hat 2025 was action over assessment. AI governance, cloud security, and data visibility are no longer just policy documents; they are enforced through executable code, CLI commands, and automated scans. Security is becoming a software engineering discipline.
The technical discourse has moved beyond whether a risk exists to how quickly it can be identified, contextualized, and remediated. The winning platforms, as noted, will be those whose outputs are directly operational. The commands outlined here are the building blocks of that operational capability, turning the conference’s lessons into command-line actions.
Prediction:
The technical focus on agentless, integrated security and AI governance will accelerate the convergence of DevOps, SecOps, and DataOps toolchains into a single, automated workflow. We will see the rise of “Security-Driven Development,” where security tools are not just gates but provide APIs and data that actively shape application architecture and business decisions. The hack, therefore, will increasingly target the integrity of the toolchains themselves—poisoning training data, compromising signing keys, or injecting vulnerabilities into open-source dependencies. The future battle will be for control over the software and AI supply chain, making the commands for software bill of materials (SBOMs), artifact signing, and dependency auditing the most critical defenses.
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IT/Security Reporter URL:
Reported By: Yohann Bauzil – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
