FA-78906 / Image orientation metadata / Open access

Big-endian orientation SHORT is read as a LONG · case 01

Photos from big-endian (MM) writers always display unrotated while little-endian files work.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The value field is read as 32 bits; in MM files a left-justified SHORT 6 becomes 0x00060000 and is rejected as out of range.

VERIFIED REPAIR

Read a 16-bit value from the first two bytes of the value field for SHORT entries.

Unsuccessful approach: Reading the second half of the field picks up padding in both byte orders.

Case contract

Parse the orientation from an APP1 Exif payload given as a list of byte values: a 6-byte "Exif\0\0" header, then a TIFF header (II little-endian or MM big-endian, magic 42 as a 16-bit value, 32-bit IFD0 offset relative to the TIFF header). Scan IFD0 12-byte entries; the first entry with tag 0x0112, type SHORT (3) and count 1 supplies the value from the first two bytes of its value field. Values outside 1..8 and a missing entry yield 1; structural problems (short payload, bad header, byte order, magic, truncated entry) yield "malformed".

Why this case matters

Camera, phone and scanner images carry an orientation hint separately from the stored pixels; galleries, thumbnailers, editors and upload pipelines must interpret it consistently or photos appear sideways, mirrored or doubly rotated.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(data):
    b = data
    if len(b) < 14 or b[:6] != [69, 120, 105, 102, 0, 0]:
        return 'malformed'
    t = b[6:]
    if t[:2] == [73, 73]:
        le = True
    elif t[:2] == [77, 77]:
        le = False
    else:
        return 'malformed'
    def u16(o):
        if o + 2 > len(t):
            return None
        return t[o] | (t[o + 1] << 8) if le else (t[o] << 8) | t[o + 1]
    def u32(o):
        if o + 4 > len(t):
            return None
        v = t[o:o + 4]
        if le:
            v = v[::-1]
        return (v[0] << 24) | (v[1] << 16) | (v[2] << 8) | v[3]
    if u16(2) != 42:
        return 'malformed'
    ifd = u32(4)
    count = u16(ifd)
    if count is None:
        return 'malformed'
    for i in range(count):
        e = ifd + 2 + 12 * i
        if e + 12 > len(t):
            return 'malformed'
        tag = u16(e)
        typ = u16(e + 2)
        cnt = u32(e + 4)
        if tag == 0x0112 and typ == 3 and cnt == 1:
            val = u32(e + 8)
            return val if 1 <= val <= 8 else 1
    return 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 4, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 8, 1, 16, 0, 3, 0, 0, 0, 1, 1, 77, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 10, 0, 0, 0, 0, 0, 0], 3], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 4, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 94, 1, 0, 0, 16, 1, 3, 0, 1, 0], 8], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 4, 1, 15, 0, 3, 0, 0, 0, 1, 1, 215, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 23, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 144, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 114, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 26, 1, 3, 0, 1, 0, 0, 0, 126, 1, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 43, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 2, 0, 15, 1, 3, 0, 1, 0, 0, 0, 73, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 37, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 155, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 58, 0, 0, 0, 0, 0, 0], 5]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 8, 1, 26, 0, 3, 0, 0, 0, 1, 1, 15, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 190, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 77, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 255, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 46, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 1, 96, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 150], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 43, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 255, 77, 77, 0, 42, 0, 0, 0, 8, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 6, 1, 26, 0, 3, 0, 0, 0, 1, 1, 200, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 17, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 9, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 111, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 24, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 64, 0, 0, 0, 0, 0, 0], 6]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 3, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 195, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 238, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 242, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 214, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 174, 0, 0, 0, 0, 0, 0], 5], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 1, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 6], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 2, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 133, 1, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 26, 0, 3, 0, 0, 0, 1, 0, 126, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0], 8]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 8, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 215, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 132, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 124], 3], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 1, 171, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 47, 0], 3], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 26, 1, 3, 0, 1, 0, 0, 0, 17, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 37, 1, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 233, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 1, 1, 15, 0, 3], 'malformed'], [[69, 120, 105, 102, 255, 225, 77, 77, 42, 0, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 43, 0, 0, 0, 8, 0, 2, 1, 26, 0, 3, 0, 0, 0, 1, 0, 60, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 82, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 3, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0], 7]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 171, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 1, 0, 18, 1, 3, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 196, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 141, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 214, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 228, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 173, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 2, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 135, 0, 0, 0, 0, 0, 0], 2]]]
labels = ["regression: SHORT value field width", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: SHORT value field width 0	`1`	`3`	Failed
repair trap 1	`8`	`8`	Passed
combined fault 2	`1`	`6`	Failed
control 3	`1`	`1`	Passed
control 4	`malformed`	`malformed`	Passed
boundary 5	`malformed`	`malformed`	Passed
boundary 6	`1`	`1`	Passed
control 7	`1`	`5`	Failed

SHA-256 / 1f87f0f005f755aa503e093a23553efea4adffd1a304e75216457fba7ccee727

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(data):
    b = data
    if len(b) < 14 or b[:6] != [69, 120, 105, 102, 0, 0]:
        return 'malformed'
    t = b[6:]
    if t[:2] == [73, 73]:
        le = True
    elif t[:2] == [77, 77]:
        le = False
    else:
        return 'malformed'
    def u16(o):
        if o + 2 > len(t):
            return None
        return t[o] | (t[o + 1] << 8) if le else (t[o] << 8) | t[o + 1]
    def u32(o):
        if o + 4 > len(t):
            return None
        v = t[o:o + 4]
        if le:
            v = v[::-1]
        return (v[0] << 24) | (v[1] << 16) | (v[2] << 8) | v[3]
    if u16(2) != 42:
        return 'malformed'
    ifd = u32(4)
    count = u16(ifd)
    if count is None:
        return 'malformed'
    for i in range(count):
        e = ifd + 2 + 12 * i
        if e + 12 > len(t):
            return 'malformed'
        tag = u16(e)
        typ = u16(e + 2)
        cnt = u32(e + 4)
        if tag == 0x0112 and typ == 3 and cnt == 1:
            val = u16(e + 10)
            return val if 1 <= val <= 8 else 1
    return 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 4, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 8, 1, 16, 0, 3, 0, 0, 0, 1, 1, 77, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 10, 0, 0, 0, 0, 0, 0], 3], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 4, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 94, 1, 0, 0, 16, 1, 3, 0, 1, 0], 8], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 4, 1, 15, 0, 3, 0, 0, 0, 1, 1, 215, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 23, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 144, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 114, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 26, 1, 3, 0, 1, 0, 0, 0, 126, 1, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 43, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 2, 0, 15, 1, 3, 0, 1, 0, 0, 0, 73, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 37, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 155, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 58, 0, 0, 0, 0, 0, 0], 5]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 8, 1, 26, 0, 3, 0, 0, 0, 1, 1, 15, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 190, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 77, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 255, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 46, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 1, 96, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 150], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 43, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 255, 77, 77, 0, 42, 0, 0, 0, 8, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 6, 1, 26, 0, 3, 0, 0, 0, 1, 1, 200, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 17, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 9, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 111, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 24, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 64, 0, 0, 0, 0, 0, 0], 6]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 3, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 195, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 238, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 242, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 214, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 174, 0, 0, 0, 0, 0, 0], 5], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 1, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 6], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 2, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 133, 1, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 26, 0, 3, 0, 0, 0, 1, 0, 126, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0], 8]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 8, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 215, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 132, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 124], 3], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 1, 171, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 47, 0], 3], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 26, 1, 3, 0, 1, 0, 0, 0, 17, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 37, 1, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 233, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 1, 1, 15, 0, 3], 'malformed'], [[69, 120, 105, 102, 255, 225, 77, 77, 42, 0, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 43, 0, 0, 0, 8, 0, 2, 1, 26, 0, 3, 0, 0, 0, 1, 0, 60, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 82, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 3, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0], 7]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 171, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 1, 0, 18, 1, 3, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 196, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 141, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 214, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 228, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 173, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 2, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 135, 0, 0, 0, 0, 0, 0], 2]]]
labels = ["regression: SHORT value field width", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: SHORT value field width 0	`1`	`3`	Failed
repair trap 1	`1`	`8`	Failed
combined fault 2	`1`	`6`	Failed
control 3	`1`	`1`	Passed
control 4	`malformed`	`malformed`	Passed
boundary 5	`malformed`	`malformed`	Passed
boundary 6	`1`	`1`	Passed
control 7	`1`	`5`	Failed

SHA-256 / 60415d520710f3ffe736a269b35f6a619765d60098584098a677c9271f8aedf8

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(data):
    b = data
    if len(b) < 14 or b[:6] != [69, 120, 105, 102, 0, 0]:
        return 'malformed'
    t = b[6:]
    if t[:2] == [73, 73]:
        le = True
    elif t[:2] == [77, 77]:
        le = False
    else:
        return 'malformed'
    def u16(o):
        if o + 2 > len(t):
            return None
        return t[o] | (t[o + 1] << 8) if le else (t[o] << 8) | t[o + 1]
    def u32(o):
        if o + 4 > len(t):
            return None
        v = t[o:o + 4]
        if le:
            v = v[::-1]
        return (v[0] << 24) | (v[1] << 16) | (v[2] << 8) | v[3]
    if u16(2) != 42:
        return 'malformed'
    ifd = u32(4)
    count = u16(ifd)
    if count is None:
        return 'malformed'
    for i in range(count):
        e = ifd + 2 + 12 * i
        if e + 12 > len(t):
            return 'malformed'
        tag = u16(e)
        typ = u16(e + 2)
        cnt = u32(e + 4)
        if tag == 0x0112 and typ == 3 and cnt == 1:
            val = u16(e + 8)
            return val if 1 <= val <= 8 else 1
    return 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 4, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 8, 1, 16, 0, 3, 0, 0, 0, 1, 1, 77, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 10, 0, 0, 0, 0, 0, 0], 3], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 4, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 94, 1, 0, 0, 16, 1, 3, 0, 1, 0], 8], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 4, 1, 15, 0, 3, 0, 0, 0, 1, 1, 215, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 23, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 144, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 114, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 26, 1, 3, 0, 1, 0, 0, 0, 126, 1, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 43, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 2, 0, 15, 1, 3, 0, 1, 0, 0, 0, 73, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 37, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 155, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 58, 0, 0, 0, 0, 0, 0], 5]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 8, 1, 26, 0, 3, 0, 0, 0, 1, 1, 15, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 190, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 77, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 255, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 46, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 1, 96, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 150], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 43, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 255, 77, 77, 0, 42, 0, 0, 0, 8, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 6, 1, 26, 0, 3, 0, 0, 0, 1, 1, 200, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 17, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 9, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 111, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 24, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 64, 0, 0, 0, 0, 0, 0], 6]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 3, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 195, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 238, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 242, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 214, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 5, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 174, 0, 0, 0, 0, 0, 0], 5], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 1, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 6], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 2, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 133, 1, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 26, 0, 3, 0, 0, 0, 1, 0, 126, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0], 8]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 5, 1, 18, 0, 4, 0, 0, 0, 2, 0, 0, 0, 8, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 215, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 0, 132, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 124], 3], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 3, 1, 15, 0, 3, 0, 0, 0, 1, 1, 171, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 47, 0], 3], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 26, 1, 3, 0, 1, 0, 0, 0, 17, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 37, 1, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 233, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 1, 1, 15, 0, 3], 'malformed'], [[69, 120, 105, 102, 255, 225, 77, 77, 42, 0, 0, 0, 0, 8, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 43, 0, 0, 0, 8, 0, 2, 1, 26, 0, 3, 0, 0, 0, 1, 0, 60, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 82, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 3, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0], 7]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 171, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 1, 0, 18, 1, 3, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 1, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 196, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 141, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 16, 0, 3, 0, 0, 0, 1, 0, 214, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 228, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 173, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 2, 1, 18, 0, 3, 0, 0, 0, 1, 0, 2, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 135, 0, 0, 0, 0, 0, 0], 2]]]
labels = ["regression: SHORT value field width", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: SHORT value field width 0	`3`	`3`	Passed
repair trap 1	`8`	`8`	Passed
combined fault 2	`6`	`6`	Passed
control 3	`1`	`1`	Passed
control 4	`malformed`	`malformed`	Passed
boundary 5	`malformed`	`malformed`	Passed
boundary 6	`1`	`1`	Passed
control 7	`5`	`5`	Passed

SHA-256 / 7dc7ac989d989825d4b2d9f9be0303d60a03a5b4457e85d9c58a18b99fe3f7c6

Verification & scope

A deterministic bounded teaching model with a stipulated contract; it makes no claim of conformance to any published specification. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:49:39.839287+00:00.

Case digest / 429aeb93fce3f0a9493541bb58060c9829a811b9ae60b95b8927a17f52df740f