Rmissax Full -
| Resource | URL | |----------|-----| | GitHub Repository | https://github.com/securelab/rmissax | | Issue Tracker | https://github.com/securelab/rmissax/issues | | Documentation Site | https://rmissax.readthedocs.io | | Discord Community | https://discord.gg/rmissax | | Plugin Marketplace | https://rmissax.io/plugins (community‑contributed plugins) |
Contributions are welcomed via pull requests. The project follows a Contributor Covenant code of conduct. rmissax full
rmissax (pronounced “R‑Miss‑A‑X”) is an open‑source reconnaissance and exploitation framework aimed at automating the discovery of misconfigurations, insecure services, and vulnerable endpoints on remote hosts. It combines a modular architecture with a set of built‑in scanners and payload generators, making it useful for penetration testers, red‑team operators, and security researchers who need a fast, scriptable way to enumerate and test a target network. If “rmissax full” is a mode/flag, enable it
Key goals of rmissax
Below is a typical “recon‑to‑exploit” pipeline using rmissax. For presets/files: load the “rmissax full” preset in
# 1️⃣ Discovery – find live hosts, open ports, and services
rmissax scan -t 10.10.0.0/16 \
--plugins portscan,service-fingerprint,sslinfo \
-o step1-discovery.json --format json
# 2️⃣ Vulnerability Check – map services to known CVEs
rmissax scan -t step1-discovery.json \
--plugins cve-search \
-o step2-vulns.json --format json
# 3️⃣ Filter for exploitable services (e.g., SMB on 445)
jq '.hosts[] | select(.services[]?.port==445)' step2-vulns.json > smb-targets.txt
# 4️⃣ Exploit – attempt unauthenticated SMB share access
rmissax exploit -t smb-targets.txt \
--plugin smb-guest \
--payload smb-read \
--output step3-exploit.json --format json
# 5️⃣ Report – generate a polished HTML report
rmissax report -i step3-exploit.json -o final-report.html --format html
Result: A single HTML file (final-report.html) that lists every host, open ports, discovered CVEs, and successful exploitation attempts, complete with screenshots (if plugins provide them) and a summary table.
run_full <- function(df,
impute_method = "auto",
n_imp = 5,
seed = NULL,
parallel = FALSE,
...)
## 1️⃣ Setup --------------------------------------------------------------
if (!is.null(seed)) set.seed(seed)
if (parallel) future::plan(future::multisession)
## 2️⃣ Detect patterns ----------------------------------------------------
pat <- detect_pattern(df, plot = FALSE)
## 3️⃣ Test missingness mechanisms ----------------------------------------
mcar_res <- test_mcar(df)
mar_res <- test_mar(df, aux_vars = select_aux(df))
mnar_res <- sensitivity_mnar(df)
## 4️⃣ Choose imputation methods -----------------------------------------
if (identical(impute_method, "auto"))
meth_tbl <- select_best_method(df, candidate_methods = c("pmm","rf","knn","norm"))
else
meth_tbl <- impute_method # assume user supplied a named list/data.frame
## 5️⃣ Multiple imputation ------------------------------------------------
imp <- impute_multiple(df,
method_tbl = meth_tbl,
n_imp = n_imp,
parallel = parallel,
seed = seed)
## 6️⃣ Diagnostics & Plots ------------------------------------------------
plots <- generate_all_plots(imp, pat)
## 7️⃣ Build the final report ---------------------------------------------
report_path <- write_report(imp,
diagnostics = list(mcar = mcar_res,
mar = mar_res,
mnar = mnar_res),
plots = plots,
output_file = "RmissAX_full_report.html")
## 8️⃣ Return a tidy list --------------------------------------------------
list(imputed_data = imp$pooled,
imputed_sets = imp$imputations,
diagnostics = list(pattern = pat,
mcar = mcar_res,
mar = mar_res,
mnar = mnar_res,
imputation = imp$diagnostics),
plots = plots,
report = report_path)
Tip: The skeleton above is publicly available in the source (
RmissAX:::run_full). Feel free to copy‑paste and adapt it for batch processing or integration with CI pipelines.