Hunt Session logo

Hunt Session

CommunityPopular
elementalsouls
hunt-session

Hunt Session Management vulnerabilities — session fixation (no regeneration on login), insufficient invalidation on logout / password-change / email-change, predictable or low-entropy session IDs, JWT-as-session with no exp/revocation, refresh-token rotation/reuse-detection gaps, OAuth/SSO session linkage, device-bound-session (DBSC) downgrade, and cookie attribute issues (Secure/HttpOnly/SameSite/__Host-). Validate with TWO real sessions (attacker A + victim B), body-diff every 200, and OOB confirmation for theft chains. Medium to Critical (fixation→admin hijack, no-invalidation→persistent ATO).

Overview

Publisherelementalsouls
RepositoryClaude-BugHunter
Skill namehunt-session
Stars
4.5K
Forks
678
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by elementalsouls on GitHub. Read the source before you install it.

Installation

Install the Hunt Session AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/elementalsouls/Claude-BugHunter.git /tmp/Claude-BugHunter
mkdir -p .claude/skills
cp -r /tmp/Claude-BugHunter/skills/hunt-session .claude/skills/hunt-session
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Session in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hunt Session on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hunt Session is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Autonomous Testing Priority

Missing HttpOnly on cookies is auto-detected — focus your active testing on lifecycle invalidation (higher impact).

Pattern 1 — Session survives logout (most common high-value finding):

  1. Login and note the session token/cookie value
  2. Call the logout endpoint (/logout, POST /api/logout, etc.)
  3. Try to use the OLD session token to access a protected resource (/api/me, /dashboard, /account)
  4. If 200 with user data → session not invalidated on logout = ATO persistence

Pattern 2 — Session not regenerated on login (session fixation):

  1. GET any page to receive a pre-authentication session token/cookie
  2. POST valid credentials to the login endpoint
  3. Compare the session token BEFORE and AFTER login
  4. If the token is unchanged → session fixation vulnerability

Pattern 3 — Session survives password change:

  1. Login → record session A value
  2. Change the password via the account settings endpoint
  3. Replay session A on a protected endpoint
  4. If 200 → token not rotated on credential change = persistent ATO (critical chain when combined with XSS/cookie theft)

Content-type: Login and session endpoints vary — use application/json for REST APIs, application/x-www-form-urlencoded for traditional web forms. Try both if the first returns an unexpected response.

Proof: A protected-resource 200 response (with user data) using a session token that should have been invalidated confirms the finding.


HUNT-SESSION — Session Management

Crown Jewel Targets

Session fixation leading to admin hijack = Critical. Session surviving a password change = High-to-Critical (persistent ATO from a stolen cookie that the victim believes they revoked by resetting their password).

Highest-value chains:

  • Session fixation — server accepts a session ID set by the client and does NOT regenerate it on login → attacker pre-plants an ID, victim authenticates, attacker rides the now-authenticated session → persistent ATO.
  • No invalidation on logout — old token still works after /logout → theft window never closes.
  • No invalidation on password / email change — a stolen session survives the victim's "I think I was hacked, let me reset" → persistent ATO. This is the single highest-paid session bug class.
  • Refresh-token reuse without rotation-detection — a leaked refresh token mints fresh access tokens forever; no reuse-detection means the legitimate user's later refresh does NOT revoke the attacker's branch.
  • Predictable / low-entropy session ID — sequential, timestamp- or userId-derived IDs → brute-force or compute other users' sessions.
  • JWT-as-session with no exp / no revocation list — stolen JWT = permanent access; logout is cosmetic.

Grounding — patterns that shaped each phase

No invented CVE/report IDs below. These are the named, publicly-documented patterns this skill encodes:

  • Session fixation, login-CSRF, no-regeneration-on-auth — OWASP WSTG-SESS-03 / WSTG-SESS-01; the classic ACROS / Mitja Kolšek session-fixation paper. Highest-impact variant: fixing the session of an SSO/admin user.
  • SameSite=Lax sibling-subdomain CSRF reaching session state — Argo CD CVE-2024-22424 (Lax cookies sent on top-level cross-site navigations from a sibling subdomain). Use this when a session cookie relies on SameSite=Lax as its only CSRF defence.
  • Refresh-token rotation & automatic reuse-detection — the Auth0/IETF OAuth-Security-BCP model: a rotated refresh token, if replayed, must invalidate the entire token family. Absence = the core bug to prove.
  • Device Bound Session Credentials (DBSC) — the W3C/Chrome DBSC draft binds a session to a TPM/device key. Test the downgrade: does the server still accept a non-bound cookie when the DBSC challenge is stripped?
  • Cookie attribute hardening — OWASP WSTG-SESS-02; __Host-/__Secure- prefixes per RFC 6265bis. Missing HttpOnly is only a finding when a real XSS/DOM sink exists (chain with hunt-xss/hunt-dom).
  • Entropy — NIST SP 800-63B requires ≥64 bits of entropy in a session identifier. Treat anything decodable to a counter/timestamp/userId as a finding regardless of length.

Cross-refs: ATO chaining → hunt-ato; JWT alg/kid tampering → hunt-api-misconfig; OAuth code/state flaws → hunt-oauth; CSRF mechanics → hunt-csrf; cookie-theft sinks → hunt-xss / hunt-dom.


Attack Surface Signals

Set-Cookie: session=...            # name varies: sid, JSESSIONID, connect.sid,
                                   # PHPSESSID, ASP.NET_SessionId, laravel_session, _csrf
/login /logout /api/login /oauth/token
/auth/refresh /api/token/refresh   # refresh-token rotation surface
/account/change-password /settings/email
?sid= ?session= in URL             # session-in-URL → leaks via Referer/logs (finding)
# Header signals worth flagging immediately:
Set-Cookie: session=abc; Path=/                 # no HttpOnly/Secure/SameSite
Set-Cookie: session=abc; SameSite=None          # None without Secure = rejected by modern browsers, but flag
Set-Cookie: __Host-sess=...; Secure; Path=/     # GOOD — hard to fixate
Sec-Session-Registration: ...                   # DBSC in play → test downgrade

Step-by-Step Hunting Methodology

Two-session rule. Every invalidation/fixation claim is proven with TWO concrete sessions captured by a real flow — attacker A and victim B — never with hardcoded placeholder strings. Helpers below capture real cookies from curl's Netscape jar.

bash
TARGET=target.com
JAR_A=$(mktemp); JAR_B=$(mktemp)

# Robust session-cookie extractor: handles #HttpOnly_ prefix lines and any
# cookie name (sid/JSESSIONID/connect.sid/PHPSESSID/...). Prints name=value.
get_cookie () {  # $1=jar  $2=name-regex (default: common session names)
  local jar="$1" re="${2:-session|sid|sess|JSESSIONID|connect\.sid|PHPSESSID|laravel_session}"
  awk -v re="$re" '
    /^#HttpOnly_/ { sub(/^#HttpOnly_/,""); }   # strip jar HttpOnly marker
    /^#/ { next }                              # skip remaining comments
    NF>=7 && $6 ~ re { print $6"="$7 }         # field6=name field7=value
  ' "$jar" | tail -1
}

Phase 1 — Session Fixation (regeneration-on-login)

bash
# Step 1: grab a pre-auth session the SERVER hands an anonymous client.
curl -s -L -c "$JAR_A" "https://$TARGET/login" -o /dev/null
PRE=$(get_cookie "$JAR_A"); echo "pre-auth: $PRE"

# Step 1b (stronger): can we FORCE an arbitrary ID? attacker-chosen value.
FIX="session=AAAAdeadbeefAAAA"

# Step 2: authenticate while CARRYING the pre-auth/forced cookie (reuse same jar).
curl -s -L -c "$JAR_A" -b "$JAR_A" -X POST "https://$TARGET/login" \
  -d "username=attacker@example.com&password=CorrectHorse1" -o /dev/null
POST=$(get_cookie "$JAR_A"); echo "post-auth: $POST"

# DECISION:
#  - If $POST == $PRE (value unchanged across the auth boundary) AND that value
#    now returns authenticated data → FIXATION. The server reused the anon ID.
#  - If the forced $FIX value is accepted and authenticates → CRITICAL fixation
#    (attacker controls the ID; no email/XSS needed to plant it).
AUTH=$(curl -s -L -b "$JAR_A" "https://$TARGET/api/me")
echo "$AUTH" | head -c 200

FP guard: a value change is not automatically safe — some apps rotate the readable cookie but keep a stable server-side session keyed by a second cookie. Diff the FULL Set-Cookie set and confirm the old value is genuinely dead (Phase 2). Also confirm /api/me returns your identity, not a generic 200/landing page.

Phase 2 — Invalidation on Logout

bash
# A logs in for real (fresh jar), capture A's live session.
curl -s -L -c "$JAR_A" -X POST "https://$TARGET/api/login" \
  -H 'Content-Type: application/json' \
  -d '{"email":"attacker@example.com","password":"CorrectHorse1"}' -o /dev/null
A=$(get_cookie "$JAR_A"); echo "A=$A"

# Baseline: what does an authenticated /api/me look like for A? (capture body, not just code)
BEFORE=$(curl -s -L -b "$JAR_A" "https://$TARGET/api/me")

# Logout A.
curl -s -L -b "$JAR_A" -X POST "https://$TARGET/api/logout" -o /dev/null

# Replay A's OLD cookie value explicitly (do NOT reuse the jar — logout may have
# overwritten it). Compare body + code against the authenticated baseline.
AFTER=$(curl -s -L -H "Cookie: $A" "https://$TARGET/api/me" -w '\n[%{http_code}]')
echo "AFTER: $AFTER"

FP discipline (mandatory):

  • Don't trust the status code. A cached/edge 200 or a generic SPA shell returns 200 for everyone. Body-diff AFTER against BEFORE — the finding is only real if AFTER still contains A's unique identity marker (email, user-id, CSRF token, account name).
  • Confirm with a negative control: a random/garbage cookie value must NOT return the same authenticated body. If garbage also yields 200 with user data, the endpoint isn't session-gated and there's no finding here.
  • Re-test after a short delay and from a different IP — some servers lazily expire on next access or pin sessions to IP.

Phase 3 — Invalidation on Password / Email Change (persistent-ATO core)

bash
# This is the real two-session flow. A = attacker holding a stolen/old session.
# B = the victim who changes their password believing it revokes access.
# (In a real engagement A is a session you legitimately captured for a TEST account
#  that you also control as B — never use a real third party.)

# 1) Log the TEST account in as session A, capture it.
curl -s -L -c "$JAR_A" -X POST "https://$TARGET/api/login" \
  -H 'Content-Type: application/json' \
  -d '{"email":"victim@example.com","password":"OldPass!1"}' -o /dev/null
SESSION_A=$(get_cookie "$JAR_A"); echo "SESSION_A=$SESSION_A"
BEFORE=$(curl -s -L -H "Cookie: $SESSION_A" "https://$TARGET/api/profile")

# 2) Log the SAME account in as session B (separate jar = "the victim's browser").
curl -s -L -c "$JAR_B" -X POST "https://$TARGET/api/login" \
  -H 'Content-Type: application/json' \
  -d '{"email":"victim@example.com","password":"OldPass!1"}' -o /dev/null

# 3) Victim (session B) changes the password.
curl -s -L -b "$JAR_B" -X POST "https://$TARGET/api/change-password" \
  -H 'Content-Type: application/json' \
  -d '{"old_password":"OldPass!1","new_password":"BrandNew!2"}' -o /dev/null

# 4) THE TEST: replay the OLD SESSION_A captured in step 1.
AFTER=$(curl -s -L -H "Cookie: $SESSION_A" "https://$TARGET/api/profile" -w '\n[%{http_code}]')
echo "AFTER pw-change: $AFTER"

Decision + FP discipline:

  • Finding is confirmed only if AFTER returns 200 and the body still carries the account's unique data (body-diff vs BEFORE). A bare 200 on a public/SPA route is not proof.
  • Run the garbage-cookie negative control again to prove the endpoint is session-gated.
  • Repeat the identical flow for email-change (/settings/email) and for logout-all-devices — apps frequently invalidate the acting session (B) but not sibling sessions (A). That sibling-survival is the exact persistent-ATO primitive hunt-ato chains.
  • Severity gate: if the change-password endpoint also lacks a current-password / MFA step-up (per hunt-mfa-bypass), A can pivot from read-only to full takeover → escalate.

Phase 4 — Cookie Attribute Analysis

bash
curl -sI -L "https://$TARGET/" | grep -i '^set-cookie'
  • HttpOnly missing → cookie reachable via document.cookie. Only a finding chained to a real XSS/DOM sink (hunt-xss/hunt-dom) — note it, don't report standalone as High.
  • Secure missing → cookie sent over cleartext HTTP; pair with hunt-tls-network (downgrade/HSTS-gap) for a network-attacker chain.
  • SameSite missing/None → CSRF reachability; SameSite=Lax is bypassable via sibling-subdomain top-level navigation (Argo CD CVE-2024-22424 class) → hand to hunt-csrf.
  • __Host- / __Secure- prefix absent → the session can be overwritten/fixated from a subdomain or non-secure context; its presence largely kills cookie-fixation, so flag the absence as the precondition for Phase 1.

Phase 5 — Session-ID Entropy

bash
# Collect a LARGE sample (200+) of freshly-issued IDs. -L is required: a 302
# /login often sets the cookie on the redirect target, not the first response.
N=200; SAMP=$(mktemp)
for i in $(seq 1 $N); do
  J=$(mktemp)
  curl -s -L -c "$J" "https://$TARGET/login" -o /dev/null
  get_cookie "$J" | cut -d= -f2- >> "$SAMP"
  rm -f "$J"
done
sort "$SAMP" | uniq -d | head            # duplicates = catastrophic (re-use)
awk '{print length($0)}' "$SAMP" | sort -n | uniq -c   # length distribution

Then analyse, don't eyeball:

  • Sequential / monotonicsort -n the decoded values; a steady +1/+N delta = predictable.
  • Decodable structurebase64 -d / hex-decode each ID and look for embedded userId, unix timestamps, or PIDs.
  • Bit entropy — feed the raw bytes to ent or dieharder; NIST SP 800-63B wants ≥64 bits. 10 samples is far too few to claim anything — gather hundreds.
  • FP guard: a long random-looking token is not proof of strength; only structural decode + a large-sample entropy estimate is. Conversely a short token with high per-char entropy may still be fine — measure, don't count characters.

Phase 6 — JWT-as-Session

bash
JWT="eyJ..."        # captured from Authorization: Bearer or a cookie
# Decode header + payload safely (base64url padding fix).
b64url(){ local s="${1//-/+}"; s="${s//_//}"; printf '%s' "$s===" | base64 -d 2>/dev/null; }
b64url "$(cut -d. -f1 <<<"$JWT")" | jq .   # header: alg, kid
b64url "$(cut -d. -f2 <<<"$JWT")" | jq .   # claims: exp, iat, sub, jti
  • exp missing or years out → no expiry. jti missing → server cannot maintain a revocation list → logout can't truly revoke.
  • Revocation test: logout, then replay the same JWT against /api/me. If it still returns the user → tokens are not server-revocable; this is the JWT-session persistence finding. Body-diff to avoid a cached 200.
  • Tampering (alg/kid/key-confusion) is owned by hunt-api-misconfig — hand off jwt_tool $JWT -T / -X a there rather than duplicating it.

Phase 7 — Refresh-Token Rotation & Reuse-Detection

bash
# 1) Obtain a refresh token (login or /oauth/token), then rotate it once.
RT1=$(curl -s -L -X POST "https://$TARGET/api/login" \
  -H 'Content-Type: application/json' \
  -d '{"email":"victim@example.com","password":"OldPass!1"}' | jq -r '.refresh_token')

# 2) Use RT1 to mint a new access token — server SHOULD return a rotated RT2.
R2=$(curl -s -L -X POST "https://$TARGET/auth/refresh" \
  -H 'Content-Type: application/json' -d "{\"refresh_token\":\"$RT1\"}")
RT2=$(jq -r '.refresh_token' <<<"$R2"); echo "rotated? RT1!=RT2 -> $([ "$RT1" != "$RT2" ] && echo yes || echo NO-ROTATION)"

# 3) REUSE-DETECTION test: replay the OLD RT1 again (simulating the leaked token).
REPLAY=$(curl -s -L -X POST "https://$TARGET/auth/refresh" \
  -H 'Content-Type: application/json' -d "{\"refresh_token\":\"$RT1\"}" -w '\n[%{http_code}]')
echo "RT1 replay: $REPLAY"

# 4) Then confirm RT2 was KILLED by the replay (correct BCP behaviour invalidates
#    the whole family). If RT2 still works after RT1 was replayed → no family-revocation.
curl -s -L -X POST "https://$TARGET/auth/refresh" \
  -H 'Content-Type: application/json' -d "{\"refresh_token\":\"$RT2\"}" -w '\n[%{http_code}]'

Findings: no rotation (RT1==RT2) = a long-lived stealable credential; rotation without reuse-detection (RT1 replay still mints tokens, or RT2 survives the replay) = the leaked-token-persistence bug per the OAuth Security BCP. OOB note: if you suspect a leaked RT via SSRF/log/JS-bundle, confirm the token's reach with hunt-ssrf/hunt-source-leak, not by guessing.

Phase 8 — OAuth/SSO Session Linkage & DBSC Downgrade

bash
# SSO linkage: after IdP callback, is the app session bound to the IdP session?
#  - Log out at the IdP only; replay the app session cookie. Still 200 with user
#    data → app session outlives the IdP session (single-logout gap).
# DBSC downgrade: if responses carry Sec-Session-Registration / Sec-Session-Id,
#  strip the device-bound proof header and replay the plain cookie:
curl -s -L -H "Cookie: $A" "https://$TARGET/api/me" -w '\n[%{http_code}]'
#  If the plain (non-bound) cookie is still accepted → device-binding is advisory,
#  not enforced → a stolen cookie defeats DBSC entirely.

Hand OAuth state/redirect_uri/code-injection to hunt-oauth; this phase only covers the session-layer binding.


Chain Table

Session findingChain toImpact
Session fixation (forced __Host--less cookie)Trick admin/SSO user into authenticating on planted IDAdmin session takeover (Critical)
No logout/password-change invalidationhunt-xss/hunt-dom cookie theft → replay surviving sessionPersistent ATO past victim's reset
Refresh token, no reuse-detectionLeaked RT (SSRF/log/bundle) → infinite access-token mintingPersistent ATO, survives password change
SameSite=Lax onlySibling-subdomain top-level nav (CVE-2024-22424 class) → CSRFState change / login-CSRF → fixation
JWT no exp/jtiStolen token, no server revocationPermanent access
DBSC downgrade acceptedSteal plain cookie despite device-bindingDefeats the only theft mitigation
Predictable IDCompute/brute another user's sessionCross-user ATO

Validation (house FP discipline)

Before claiming ANY session finding:

  • Two real sessions, not placeholders — every fixation/invalidation claim uses A and B captured by the curl flows above.
  • Body-diff, never status-only — a 200 means nothing without the account's unique identity marker present in the body, diffed against the authenticated baseline.
  • Negative control — a garbage/random cookie must FAIL where your "surviving" cookie succeeds; otherwise the endpoint isn't session-gated and it's a non-finding.
  • Cache/edge check — re-request with a cache-buster and from a second IP; rule out an edge-cached or IP-pinned 200.
  • OOB for theft chains — when the impact depends on exfiltrating a cookie/token (XSS, SSRF, log leak), confirm receipt out-of-band (Collaborator) rather than asserting it.
  • Static-vs-stateHttpOnly/Secure/SameSite absence is a policy observation; only report as High once paired with a real exploit primitive (XSS, network-MITM, CSRF). Standalone attribute gaps are Low/Informational.

Severity:

  • Session fixation → admin/SSO takeover: Critical
  • No invalidation on password/email change, or refresh-token reuse without detection: High → Critical (escalate if MFA/step-up also absent)
  • Predictable/duplicate session ID: High
  • No invalidation on logout: Medium → High (depends on theft vector)
  • Missing HttpOnly/SameSite standalone: Low/Informational until chained

Frequently asked questions

What does the Hunt Session AI skill do?

Hunt Session Management vulnerabilities — session fixation (no regeneration on login), insufficient invalidation on logout / password-change / email-change, predictable or low-entropy session IDs, JWT-as-session with no exp/revocation, refresh-token rotation/reuse-detection gaps, OAuth/SSO session linkage, device-bound-session (DBSC) downgrade, and cookie attribute issues (Secure/HttpOnly/SameSite/__Host-). Validate with TWO real sessions (attacker A + victim B), body-diff every 200, and OOB confirmation for theft chains. Medium to Critical (fixation→admin hijack, no-invalidation→persistent...

Why use Hunt Session on TypingMind?

Because you install it once and use it with any model. Hunt Session is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hunt Session in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elementalsouls/Claude-BugHunter/tree/main/skills/hunt-session. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Session?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hunt Session?

As many as you like. As long as a model supports skills, you can use Hunt Session with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hunt Session AI skill free?

Yes. It is published on GitHub by elementalsouls under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇