FA-78926 / Image orientation metadata / Open access

Orientation entries of type LONG are trusted · case 01

A malformed LONG orientation entry shadows a later valid SHORT entry.

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

ROOT CAUSE

The type filter accepts LONG (4), and the SHORT reader then returns only half of the long value.

VERIFIED REPAIR

Accept only SHORT (type 3) orientation entries and keep scanning otherwise.

Unsuccessful approach: Accepting any non-zero type admits ASCII and RATIONAL entries too.

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 in (3, 4) 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, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 164, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 152, 0, 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, 73, 73, 42, 0, 8, 0, 0, 0, 2, 0, 16, 1, 3, 0, 1, 0, 0, 0, 247, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 124, 1, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 253, 0, 0, 0, 0, 0, 0], 8]], [[[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 77, 1, 0, 0, 16, 1, 3, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 41, 1, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 174, 0, 0, 0, 0, 0, 0, 0], 1], [[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, 42, 0, 10, 0, 0, 0, 0, 0, 3, 0, 26, 1, 3, 0, 1, 0, 0, 0, 115, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 111, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], 8], [[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, 73, 73, 42, 0, 8, 0, 0, 0, 1, 0, 18, 1, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], 4], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 68, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 35, 1, 0, 0, 0, 0, 0, 0], 6]], [[[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 171, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 106, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], 4], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 108, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 1, 0, 9, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 115, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 244, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 186, 0, 0, 0, 0, 0, 0], 1], [[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, 3, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 202, 0, 0, 0, 15, 1, 3, 0, 1], 8], [[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, 14, 0, 0, 0, 0, 0, 0, 0, 5, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 6, 1, 15, 0, 3, 0, 0, 0, 1, 0, 102, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 141, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 73, 0, 0, 0, 0, 0, 0], 8]], [[[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, 4, 0, 0, 0, 1, 0, 0, 0, 6, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 7, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 189, 0, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 26, 0, 3, 0, 0, 0, 1, 1, 79, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 177, 0, 0, 1, 26, 0, 3, 0, 0], 'malformed'], [[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, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 147, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 88, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 151, 1, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 18, 1, 3, 0, 2, 0, 0, 0, 8, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 6, 1, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 125, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 225, 1, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], 5], [[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, 18, 0, 3, 0, 0, 0, 1, 0, 2, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 127, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 210, 0, 0, 0, 0, 0, 0], 2]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 8, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 37, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 74, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 176], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 241, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 15, 1, 3, 0, 1, 0, 0, 0, 51, 1, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 55], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 2, 0, 15, 1, 3, 0, 1, 0, 0, 0, 215, 1, 0, 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, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 173, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 83, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 181, 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, 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, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 30, 0, 0], 'malformed']]]
labels = ["regression: orientation entry type filter", "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: orientation entry type filter 0	`1`	`3`	Failed
repair trap 1	`3`	`6`	Failed
combined fault 2	`1`	`1`	Passed
control 3	`malformed`	`malformed`	Passed
control 4	`malformed`	`malformed`	Passed
boundary 5	`1`	`1`	Passed
boundary 6	`1`	`1`	Passed
control 7	`1`	`8`	Failed

SHA-256 / 322bc6d1328c8d1b5b38ccd48bc5e4b7e9be22475a235afaf245168221041217

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 != 0 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, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 164, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 152, 0, 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, 73, 73, 42, 0, 8, 0, 0, 0, 2, 0, 16, 1, 3, 0, 1, 0, 0, 0, 247, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 124, 1, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 253, 0, 0, 0, 0, 0, 0], 8]], [[[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 77, 1, 0, 0, 16, 1, 3, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 41, 1, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 174, 0, 0, 0, 0, 0, 0, 0], 1], [[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, 42, 0, 10, 0, 0, 0, 0, 0, 3, 0, 26, 1, 3, 0, 1, 0, 0, 0, 115, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 111, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], 8], [[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, 73, 73, 42, 0, 8, 0, 0, 0, 1, 0, 18, 1, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], 4], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 68, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 35, 1, 0, 0, 0, 0, 0, 0], 6]], [[[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 171, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 106, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], 4], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 108, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 1, 0, 9, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 115, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 244, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 186, 0, 0, 0, 0, 0, 0], 1], [[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, 3, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 202, 0, 0, 0, 15, 1, 3, 0, 1], 8], [[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, 14, 0, 0, 0, 0, 0, 0, 0, 5, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 6, 1, 15, 0, 3, 0, 0, 0, 1, 0, 102, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 141, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 73, 0, 0, 0, 0, 0, 0], 8]], [[[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, 4, 0, 0, 0, 1, 0, 0, 0, 6, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 7, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 189, 0, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 26, 0, 3, 0, 0, 0, 1, 1, 79, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 177, 0, 0, 1, 26, 0, 3, 0, 0], 'malformed'], [[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, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 147, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 88, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 151, 1, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 18, 1, 3, 0, 2, 0, 0, 0, 8, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 6, 1, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 125, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 225, 1, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], 5], [[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, 18, 0, 3, 0, 0, 0, 1, 0, 2, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 127, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 210, 0, 0, 0, 0, 0, 0], 2]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 8, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 37, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 74, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 176], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 241, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 15, 1, 3, 0, 1, 0, 0, 0, 51, 1, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 55], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 2, 0, 15, 1, 3, 0, 1, 0, 0, 0, 215, 1, 0, 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, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 173, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 83, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 181, 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, 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, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 30, 0, 0], 'malformed']]]
labels = ["regression: orientation entry type filter", "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: orientation entry type filter 0	`1`	`3`	Failed
repair trap 1	`3`	`6`	Failed
combined fault 2	`1`	`1`	Passed
control 3	`malformed`	`malformed`	Passed
control 4	`malformed`	`malformed`	Passed
boundary 5	`1`	`1`	Passed
boundary 6	`1`	`1`	Passed
control 7	`1`	`8`	Failed

SHA-256 / 6c187bfedc3750016952dad38f67377db738d16229ca4b8fcf64d5853764d5c3

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, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 164, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 152, 0, 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, 73, 73, 42, 0, 8, 0, 0, 0, 2, 0, 16, 1, 3, 0, 1, 0, 0, 0, 247, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 124, 1, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 3, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 253, 0, 0, 0, 0, 0, 0], 8]], [[[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 77, 1, 0, 0, 16, 1, 3, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 10, 0, 0, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 41, 1, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 174, 0, 0, 0, 0, 0, 0, 0], 1], [[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, 42, 0, 10, 0, 0, 0, 0, 0, 3, 0, 26, 1, 3, 0, 1, 0, 0, 0, 115, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 111, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], 8], [[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, 73, 73, 42, 0, 8, 0, 0, 0, 1, 0, 18, 1, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], 4], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 68, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 35, 1, 0, 0, 0, 0, 0, 0], 6]], [[[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 171, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 106, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], 4], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 108, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0], 1], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 1, 0, 9, 0, 0, 1, 26, 0, 3, 0, 0, 0, 1, 1, 115, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 244, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 186, 0, 0, 0, 0, 0, 0], 1], [[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, 3, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 202, 0, 0, 0, 15, 1, 3, 0, 1], 8], [[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, 14, 0, 0, 0, 0, 0, 0, 0, 5, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 6, 1, 15, 0, 3, 0, 0, 0, 1, 0, 102, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 141, 0, 0, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 73, 0, 0, 0, 0, 0, 0], 8]], [[[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, 4, 0, 0, 0, 1, 0, 0, 0, 6, 1, 18, 0, 3, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 3, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 7, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 189, 0, 0, 0, 0, 0, 0, 0], 7], [[69, 120, 105, 102, 0, 0, 77, 77, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 3, 1, 26, 0, 3, 0, 0, 0, 1, 1, 79, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 177, 0, 0, 1, 26, 0, 3, 0, 0], 'malformed'], [[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, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 147, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 88, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 151, 1, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 18, 1, 3, 0, 2, 0, 0, 0, 8, 0, 0, 0, 26, 1, 3, 0, 1, 0, 0, 0, 6, 1, 0, 0, 15, 1, 3, 0, 1, 0, 0, 0, 125, 1, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 225, 1, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], 5], [[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, 18, 0, 3, 0, 0, 0, 1, 0, 2, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 0, 127, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 210, 0, 0, 0, 0, 0, 0], 2]], [[[69, 120, 105, 102, 0, 0, 77, 77, 0, 42, 0, 0, 0, 10, 0, 0, 0, 2, 1, 18, 0, 4, 0, 0, 0, 1, 0, 0, 0, 8, 1, 18, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0], 6], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 4, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 37, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 74, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 176], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 241, 0, 0, 0, 0, 0, 0, 0], 'malformed'], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 15, 1, 3, 0, 1, 0, 0, 0, 51, 1, 0, 0, 18, 1, 3, 0, 1, 0, 0, 0, 8, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 55], 8], [[69, 120, 105, 102, 0, 0, 73, 73, 42, 0, 8, 0, 0, 0, 2, 0, 15, 1, 3, 0, 1, 0, 0, 0, 215, 1, 0, 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, 8, 0, 4, 1, 18, 0, 3, 0, 0, 0, 2, 0, 8, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 0, 173, 0, 0, 1, 16, 0, 3, 0, 0, 0, 1, 1, 83, 0, 0, 1, 15, 0, 3, 0, 0, 0, 1, 1, 181, 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, 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, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 18, 1, 4, 0, 1, 0, 0, 0, 3, 0, 0, 0, 16, 1, 3, 0, 1, 0, 0, 0, 30, 0, 0], 'malformed']]]
labels = ["regression: orientation entry type filter", "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: orientation entry type filter 0	`3`	`3`	Passed
repair trap 1	`6`	`6`	Passed
combined fault 2	`1`	`1`	Passed
control 3	`malformed`	`malformed`	Passed
control 4	`malformed`	`malformed`	Passed
boundary 5	`1`	`1`	Passed
boundary 6	`1`	`1`	Passed
control 7	`8`	`8`	Passed

SHA-256 / 31bc97b0924a553da8988a0cbc18bc3b10a04f60ff5496615613cf9072ebaceb

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 2026-09-29T14:49:39.857600+00:00.

Case digest / de8814a8578383c372c22d80b2a7128aebb952c8461fc49d43987cae8dbe2b16